library(tidyverse)
library(ggpubr)
library(survival)
library(survminer)
library(patchwork)
bulk2046 <- read_csv('BALL2046_DevState_Updated_April2024_Fusions_MRD_AZ.csv')

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_character(),
  Age = col_double(),
  WBC = col_double(),
  oscensor = col_double(),
  ostime = col_double(),
  efscensor = col_double(),
  efstime = col_double(),
  HSC_MPP = col_double(),
  Myeloid_Prog = col_double(),
  Pre_pDC = col_double(),
  Early_Lymphoid = col_double(),
  Pro_B = col_double(),
  Pre_B = col_double(),
  Mature_B = col_double(),
  T_NK = col_double(),
  Monocyte = col_double(),
  Erythroid = col_double()
)
ℹ Use `spec()` for the full column specifications.
bulk2046 

PCA on Dev State Abundance

Include the four main lineages along B cell development

LineageScores <- bulk2046 %>% select(PatientID, HSC_MPP, Early_Lymphoid, Pro_B, Pre_B) %>%   
  column_to_rownames('PatientID') %>% data.matrix()
bulk2046$PC1 <- prcomp(LineageScores)[5]$x[,1]
prcomp(LineageScores, scale=T, center=T)
Standard deviations (1, .., p=4):
[1] 1.2839839 1.1670773 0.8355012 0.5396791

Rotation (n x k) = (4 x 4):
                      PC1        PC2        PC3        PC4
HSC_MPP         0.5700131 -0.4172374  0.4470556 -0.5487615
Early_Lymphoid  0.5125228 -0.4916068 -0.4791663  0.5157933
Pro_B          -0.4753292 -0.5306387 -0.4971588 -0.4952958
Pre_B          -0.4318188 -0.5501439  0.5686599  0.4330129

Principal Component 1 is a Multipotency Score

DevState_PCA <- data.frame(prcomp(LineageScores, scale=T, center=T)[2]$rotation) %>% rownames_to_column('DevState')
color <- ifelse(DevState_PCA$PC1 > 0, 'darkgreen', 'darkorange')


DevState_PCA %>% 
  mutate(DevState = factor(DevState %>% str_replace('HSC_MPP', 'HSC/MPP') %>% str_replace('_B','-B') %>% str_replace('_',' '), 
                           levels = rev(c("HSC/MPP", "Early Lymphoid", "Pro-B", "Pre-B")))) %>% 
  ggplot(aes(x = DevState, y = PC1)) +
  geom_bar(stat = "identity", show.legend = FALSE, fill = color, color = "white") +
  geom_hline(yintercept = 0, color = 1, lwd = 0.2) +
  geom_text(aes(label = DevState, # Text with groups
                hjust = ifelse(PC1 < 0, 1.25, -0.15),
                vjust = 0.5), size = 3.5) +
  xlab("Developmental State") + ylab("PC1 Feature Loadings") +
  scale_y_continuous(breaks = seq(-1, 1, by = 0.25), limits = c(-0.8, 0.8)) +
  coord_flip() +
  theme_minimal() +
  theme(axis.text.y = element_blank(),  # Remove Y-axis texts
        axis.ticks.y = element_blank(), # Remove Y-axis ticks
        panel.grid.major.y = element_blank(),
        panel.grid.minor.x = element_blank()) # Remove horizontal grid

ggsave('BALL_MultipotencyScore_Figures/MultipotencyScore_PC1_FeatureLoadings.pdf', height = 3.2, width=6)

Derive gene signature for estimating PC1

train_LASSO <- function(x_train, y_train, alpha = 1){
  
  train_y <- y_train$PC1
  
  # Perform Lasso regression with LOOCV 
  model <- cv.glmnet(x = x_train, y = train_y, nfold = 10, family = 'gaussian', alpha = alpha, maxit=1000000, standardize=FALSE)
  #plot(model)

  return(model)
}

evaluate_model <- function(model, x_val, anno_val, lambda, 
                           feature_name, iteration, foldname){

  # Create score classification with survival and get covariates
  pred_y <- predict(model, x_val, s = lambda) %>% data.frame()
  colnames(pred_y) <- 'PredScore'
  pred_y <- pred_y %>% rownames_to_column('Patient') %>% 
    # add anno to get covariates
    left_join(anno_val, by = 'Patient')
  
  # Calculate correlation in validation set
  pearson <- cor(pred_y$PredScore, pred_y$PC1, method = 'pearson')
  spearman <- cor(pred_y$PredScore, pred_y$PC1, method = 'spearman')
  
  # Summary Metrics
  summary_metrics <- data.frame(
    'model_id' = paste0(feature_name, '_iter', iteration, '_', foldname),
    'lambda' = lambda,
    'model_size' = sum(coef(model, s = lambda)!=0),
    'pearson' = pearson,
    'spearman' = spearman,
    'features' = feature_name,
    'iteration' = iteration,
    'foldname' = foldname
  )
  return(summary_metrics)
}


gridsearch_lasso <- function(expr_train, expr_val, anno_train, anno_val, features, feature_name,
                             iteration, foldname, summary_metrics){
  
  # Filter expr matrix for feature set
  x_train <- expr_train[, colnames(expr_train) %in% features]
  x_val <- expr_val[, colnames(expr_val) %in% features]

  # Train LASSO 
  model <- train_LASSO(x_train, anno_train)

  # Get summary metrics for lambda.min and lambda.1se
  for(lambda in c('lambda.min', 'lambda.1se')){
    summary_metrics <- summary_metrics %>% rbind(
      evaluate_model(model = model, x_val = x_val, anno_val = anno_val, lambda = lambda, 
                     feature_name = feature_name, iteration = iteration, foldname = foldname))
  }
  
  return(summary_metrics)
}


nestedCV_regression <- function(train_anno, train_expr, iteration, feature_sets, summary_metrics){
  # set up random seed and shuffle data 
  set.seed(iteration)
  train_anno <- train_anno[sample(nrow(train_anno)),]
  train_expr <- train_expr[sample(nrow(train_expr)),]
  
  ## 10-fold outer cross validation
  folds <- rsample::vfold_cv(train_anno, 10)
  for(outer_cv in 1:10){
    # fold ID
    foldname <- folds$id[[outer_cv]]
    # get anno splits
    anno_train <- analysis(folds$splits[[outer_cv]])
    anno_val <- assessment(folds$splits[[outer_cv]])
    # get expr splits
    expr_train <- train_expr[anno_train$Patient,]
    expr_val <- train_expr[anno_val$Patient,]
    
    # Iterate through feature set and run gridsearch to train survival functions
    for(feature_name in names(feature_sets)){
      # get feature list
      features <- feature_sets[[feature_name]]
      # run gridsearch and get results
      summary_metrics <- gridsearch_lasso(expr_train = expr_train, expr_val = expr_val, anno_train = anno_train, anno_val = anno_val, 
                                  features = features, feature_name = feature_name, iteration = iteration, foldname = foldname,
                                  summary_metrics = summary_metrics)
    }
  }
  return(summary_metrics)
}
bulk2046_vst <- readRDS('../BALL2046_BulkRNA_vst.rds')
bulk2046_vst %>% dim()
[1] 35289  2046
# Train from genes used to predict the four bdev lineage populations
modelweights <- read_csv("../NMF_Lasso_ModelWeights.csv") %>% select(Gene, HSC_MPP, Early_Lymphoid, Pro_B, Pre_B) %>% 
  rowwise() %>% mutate(sumweights = sum(HSC_MPP, Early_Lymphoid, Pro_B, Pre_B)) %>% filter(sumweights != 0) %>% select(-sumweights) 

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  Gene = col_character(),
  HSC_MPP = col_double(),
  Myeloid_Prog = col_double(),
  Pre_pDC = col_double(),
  Early_Lymphoid = col_double(),
  Pro_B = col_double(),
  Pre_B = col_double(),
  Mature_B = col_double(),
  Erythroid = col_double(),
  Monocyte = col_double(),
  T_NK = col_double()
)
bulk2046_vst_filtered <- bulk2046_vst[modelweights$Gene,] 
bulk2046_vst_filtered %>% dim()
[1]  115 2046
library(tidymodels)
library(glmnet)

CVoutput <- data.frame()
train_x <- bulk2046_vst_filtered[,bulk2046$SampleID_old] %>% t()
train_y <- bulk2046 %>% select(Patient = SampleID_old, PC1)# %>% mutate(PC1 = -PC1)
featurespace <- list('ModelWeights115' = modelweights$Gene)
temp_output <- data.frame()


for(iteration in 1:10){
  print(paste0('iteration ', iteration))
  CVoutput <- nestedCV_regression(train_anno = train_y, train_expr = train_x, iteration = iteration, feature_sets = featurespace, 
                                summary_metrics = CVoutput) 
}
[1] "iteration 1"
[1] "iteration 2"
[1] "iteration 3"
[1] "iteration 4"
[1] "iteration 5"
[1] "iteration 6"
[1] "iteration 7"
[1] "iteration 8"
[1] "iteration 9"
[1] "iteration 10"
## annotate and add to final output
CVoutput 
CVoutput %>% write_csv('RepNestedCV_results_PC1multipotency_Regression.csv')
train_LASSO <- function(x_train, y_train, alpha = 1){
  
  train_y <- y_train$PC1
  
  # Perform Lasso regression with LOOCV 
  model <- cv.glmnet(x = x_train, y = train_y, nfold = 10, family = 'gaussian', alpha = alpha, maxit=1000000, standardize=FALSE)
  #plot(model)

  return(model)
}
model <- train_LASSO(train_x, y_train = train_y)

# PC1 model weights
PC1_modelweights <- data.frame()

PC1_modelweights <- model %>% coef(s = 'lambda.1se') %>% data.matrix() %>% 
      data.frame() %>% dplyr::rename(Weight = s1) %>% rownames_to_column('Gene') %>% 
      tail(-1) %>% filter(Weight != 0) %>% arrange(-Weight) 
    
PC1_modelweights <- PC1_modelweights %>% select(Gene, Weight)
PC1_modelweights
# Create final model matrix
modelweights_withMultipotency <- read_csv("../NMF_Lasso_ModelWeights.csv")  %>% 
  left_join(PC1_modelweights %>% select(Gene, Multipotency_Score = Weight)) %>% 
  replace(is.na(.), 0) 

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  Gene = col_character(),
  HSC_MPP = col_double(),
  Myeloid_Prog = col_double(),
  Pre_pDC = col_double(),
  Early_Lymphoid = col_double(),
  Pro_B = col_double(),
  Pre_B = col_double(),
  Mature_B = col_double(),
  Erythroid = col_double(),
  Monocyte = col_double(),
  T_NK = col_double()
)
Joining with `by = join_by(Gene)`
modelweights_withMultipotency %>% write_csv("DevState_Lasso_ModelWeights_withMultipotencyScore_May2024.csv")
modelweights_withMultipotency
calculate_DevState_scores = function(query, modelweights, scale = TRUE, sampleID = 'Patient'){
  
  # Check for overlap with model genes and query genes
  querygenes <- rownames(query)
  modelweights_missing <- sum(!(modelweights$Gene %in% querygenes))
  # check for missing genes
  if(modelweights_missing > 0){
    print(paste0('Warning: ', modelweights_missing, ' genes from Dev State models are missing from query dataset'))
  }
  
  # filter model weights
  modelweights <- modelweights %>% filter(Gene %in% querygenes)
  modelweights_mat <- modelweights %>% column_to_rownames('Gene') %>% data.matrix()
  
  # multiply query by Dev State lasso weights
  scored <- (t(query[modelweights$Gene,]) %*% modelweights_mat) %>% data.matrix() 
  if(scale == TRUE){
    scored <- scale(scored)
  }
  scored <- scored %>% as.data.frame() %>% rownames_to_column(sampleID) 
  
  return(scored)
}

Calculate in bulk2046 and validate

bulk2046 <- bulk2046 %>% 
  left_join( calculate_DevState_scores(bulk2046_vst, modelweights_withMultipotency, scale = T, sampleID = 'SampleID_old') %>% select(SampleID_old, Multipotency_Score) ) 
Joining with `by = join_by(SampleID_old)`
bulk2046
# Load repeated nested cross-validation (10-fold, 10 repeats) results 
CVoutput <- read_csv('RepNestedCV_results_PC1multipotency_Regression.csv')

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  model_id = col_character(),
  lambda = col_character(),
  model_size = col_double(),
  pearson = col_double(),
  spearman = col_double(),
  features = col_character(),
  iteration = col_double(),
  foldname = col_character()
)
PC1model_CVplot <- CVoutput %>% filter(lambda == 'lambda.1se') %>%
  mutate(model = '') %>% 
  ggplot(aes(x = model, y = pearson^2)) +
  ylab('Pearson Correlation with PC1') + xlab('PC1 Models\n(Cross-Validation)') + 
  geom_boxplot(outlier.size=0) + ggbeeswarm::geom_quasirandom(width=0.32) + theme_pubr() + theme(axis.ticks.x = element_blank())

# Get correlation across full cohort
bulk2046_PC1_Multipotency_plot <- bulk2046 %>% 
  ggplot(aes(x = Multipotency_Score, y = PC1)) + 
  xlab('B-ALL Multipotency Score (99 Genes)') + 
  geom_point(size=0.7) + stat_cor(r.digits = 4) + theme_pubr()

PC1model_CVplot + bulk2046_PC1_Multipotency_plot + plot_layout(widths=c(0.25,0.75))
ggsave('BALL_MultipotencyScore_Figures/MultipotencyScore_PC1_ModelPerformance.pdf', height = 4.5, width=8)

bulk2046 %>% write_csv('BALL2046_DevState_Updated_May2024_AZ.csv')
bulk2046

Multipotency Score in Normal B cell Development

library(DESeq2)
BDev_pseudobulk <- readRDS('../../BDevelopment_Pseudobulk_byTissue_CellType.rds')
# vst normalize
BDev_pseudobulk[['RNA']]@data <- DESeqDataSetFromMatrix(BDev_pseudobulk[['RNA']]@counts, colData = BDev_pseudobulk@meta.data, design = ~1) %>% vst() %>% assay()
converting counts to integer mode
BDev_pseudobulk[['RNA']]@data[1:10,1:5]
          Ainciburu2022__Bone Marrow__CLP Ainciburu2022__Bone Marrow__Early_GMP Ainciburu2022__Bone Marrow__HSC_MPP Ainciburu2022__Bone Marrow__HSC_MPP_Unk
FAM87B                           3.628009                              3.628009                            3.628009                                3.628009
LINC00115                        4.180853                              4.025843                            4.093941                                4.164805
FAM41C                           4.465845                              4.359141                            4.282165                                4.318407
SAMD11                           3.628009                              3.841141                            3.764684                                3.628009
NOC2L                            5.541047                              5.564924                            5.255788                                4.725258
KLHL17                           3.628009                              3.771774                            3.679684                                3.628009
PLEKHN1                          3.628009                              3.628009                            3.701084                                3.628009
PERM1                            3.628009                              3.692324                            3.628009                                3.628009
HES4                             3.628009                              3.628009                            3.628009                                3.628009
ISG15                            5.918573                              5.790165                            5.371075                                5.569232
          Ainciburu2022__Bone Marrow__Immature_B
FAM87B                                  3.628009
LINC00115                               3.628009
FAM41C                                  3.628009
SAMD11                                  3.628009
NOC2L                                   3.628009
KLHL17                                  3.628009
PLEKHN1                                 3.628009
PERM1                                   3.628009
HES4                                    3.628009
ISG15                                   3.628009
# Calculate Multipotency score
BDev_pseudobulk@meta.data <- bind_cols(BDev_pseudobulk@meta.data, 
                                       calculate_DevState_scores(BDev_pseudobulk[['RNA']]@data, modelweights_withMultipotency, scale = T, sampleID = 'Sample') %>% column_to_rownames('Sample'))
BDev_pseudobulk@meta.data
BDev_pseudobulk@meta.data %>% 
  filter(nCells >= 50) %>% 
  mutate(BDevelopment_CellType_Comprehensive = BDevelopment_CellType_Comprehensive %>% 
           factor(levels = c('HSC/MPP', 'MPP-MyLy', 'LMPP', 'Early GMP', 'Pre-pDC', 'Pre-pDC Cycling', 'pDC', 'MLP', 
                             'CLP', 'Pre-Pro-B', 'Pro-B VDJ', 'Pro-B Cycling 1', 'Pro-B Cycling 2',  # 'Pre-Pro-B Cycling', 
                             'Large Pre-B 1', 'Large Pre-B 2', 'Small Pre-B', 'Immature B', 'Mature B'))) %>%  #Mature B Cycling
  mutate(`Developmental State` = ifelse(BDevelopment_CellType_Comprehensive %in% c('HSC/MPP', 'MPP-MyLy', 'LMPP'), 'HSC/MPP',
                                        ifelse(BDevelopment_CellType_Comprehensive %in% c('Early GMP'), 'Myeloid Progenitor',
                                               ifelse(BDevelopment_CellType_Comprehensive %in% c('Pre-pDC', 'Pre-pDC Cycling', 'pDC'), 'Pre-pDC',
                                                   ifelse(BDevelopment_CellType_Comprehensive %in% c('MLP', 'CLP', 'Pre-Pro-B', 'Pre-Pro-B Cycling'), 'Early Lymphoid',
                                                          ifelse(BDevelopment_CellType_Comprehensive %in% c('Pro-B VDJ', 'Pro-B Cycling 1', 'Pro-B Cycling 2'), 'Pro-B',
                                                                 ifelse(BDevelopment_CellType_Comprehensive %in% c('Large Pre-B 1', 'Large Pre-B 2', 'Small Pre-B'), 'Pre-B',
                                                                        ifelse(BDevelopment_CellType_Comprehensive %in% c('Immature B', 'Mature B', 'Mature B Cycling'), 'Mature B', 'NA'))))))) %>% 
           factor(levels = c('HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B', 'Mature B'))) %>% 
  filter(!is.na(BDevelopment_CellType_Comprehensive)) %>% 
  ggplot(aes(x = BDevelopment_CellType_Comprehensive, y = Multipotency_Score, fill = `Developmental State`)) + 
  geom_hline(yintercept=0, lty=5, size=0.5, alpha=0.8) + geom_boxplot(outlier.size=0.3) + 
  theme_pubr(legend='right') + theme(axis.text.x = element_text(angle=90, hjust=1, size=10.5)) + 
  xlab('\nNormal Population (B Cell Development Atlas)') + ylab('B-ALL Multipotency Score') + 
  scale_fill_brewer(palette = 'Dark2')

ggsave('BALL_MultipotencyScore_Figures/MultipotencyScore_Normal_BDev_pseudobulkScores.pdf', height = 5, width=9.5)

Evaluate 99-gene Multipotency Score within 2046 cohort

bulk2046 <- read_csv('BALL2046_DevState_Updated_May2024_AZ.csv')

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_character(),
  Age = col_double(),
  WBC = col_double(),
  oscensor = col_double(),
  ostime = col_double(),
  efscensor = col_double(),
  efstime = col_double(),
  HSC_MPP = col_double(),
  Myeloid_Prog = col_double(),
  Pre_pDC = col_double(),
  Early_Lymphoid = col_double(),
  Pro_B = col_double(),
  Pre_B = col_double(),
  Mature_B = col_double(),
  T_NK = col_double(),
  Monocyte = col_double(),
  Erythroid = col_double(),
  PC1 = col_double(),
  Multipotency_Score = col_double()
)
ℹ Use `spec()` for the full column specifications.

Evaluate on Clinical Risk Group

bulk2046$Risk_Group %>% table()
.
       Adult          AYA Childhood HR Childhood SR 
         385          430          680          527 
p <- bulk2046 %>% 
    select(Patient, Risk_Group, Multipotency_Score) %>% pivot_longer(-c(Patient, Risk_Group), names_to='Lineage', values_to='Score') %>%
    filter(Risk_Group != 'NA') %>% mutate(Risk_Group = Risk_Group %>% factor(levels = c('Childhood SR', 'Childhood HR', 'AYA', 'Adult'))) %>% 
    filter(Lineage %in% c('Multipotency_Score')) %>% 
    mutate(Lineage = Lineage %>% str_replace('_', ' ')) %>% 
    ggplot(aes(x = Risk_Group, y = Score, fill = Risk_Group)) + 
    geom_hline(yintercept = 0, alpha = 0.7, lty = 2) + 
    geom_boxplot(outlier.size=0) + ggbeeswarm::geom_quasirandom(aes(size = Risk_Group), width=0.3) + 
    facet_wrap(.~Lineage, scales='free', ncol=5) + 
    theme_pubr(legend = 'none') + 
    scale_fill_brewer(palette = 'Dark2') + 
    theme(strip.text.x = element_text(size = 14), axis.text.x = element_text(size = 11), axis.text.y = element_text(size = 12), axis.title = element_text(size = 12.5)) + 
    scale_size_manual(values = c(0.2, 0.2, 0.3, 0.3)) + xlab('\nClinical Risk Group') + ylab('B-ALL Multipotency Score') + 
    stat_compare_means(comparisons = list(c('Childhood SR', 'Childhood HR'), c('Childhood HR', 'AYA'), c('AYA', 'Adult')))

p
ggsave('BALL_MultipotencyScore_Figures/RiskGroup_2022patients_Multipotency_Score.pdf', device = 'pdf', height = 5.5, width = 4.8)

sum(!is.na(bulk2046$Age ))
[1] 2019
p <- bulk2046 %>% select(Age, Multipotency_Score) %>% pivot_longer(-Age) %>% 
    mutate(`Developmental State` = name %>% str_replace('_',' ')) %>%
    #mutate(`Developmental State` = ifelse(name %>% str_detect('Early'), 'Early Lymphoid',
    #                                     ifelse(name %>% str_detect('Pro_B'), 'Pro-B', 'NA'))) %>% 
    ggplot(aes(x = Age, y = value, color = `Developmental State`)) + 
    geom_smooth(method = 'loess', se = T, size = 2) + #geom_point(size = 0.3, alpha = 0.5) + 
    scale_x_sqrt(breaks = c(1, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80)) + 
    scale_color_brewer(palette = 'Dark2') + 
    geom_hline(yintercept = 0, lty = 3) + 
    geom_vline(xintercept = 1, lty = 3) + geom_vline(xintercept = 15, lty = 3) + geom_vline(xintercept = 40, lty = 3) + 
    xlab('Age at Diagnosis (Years)') + ylab('B-ALL Multipotency Score') +
    ggpubr::theme_pubr(legend = 'top') + theme(legend.title = element_blank())
        
p
ggsave('BALL_MultipotencyScore_Figures/Age_vs_MultipotencyScore_2019patients.pdf', height = 4.2, width = 5.5)

p <- bulk2046 %>% select(Age, Multipotency_Score) %>% pivot_longer(-Age) %>% 
    mutate(`Developmental State` = name %>% str_replace('_',' ')) %>%
    #mutate(`Developmental State` = ifelse(name %>% str_detect('Early'), 'Early Lymphoid',
    #                                     ifelse(name %>% str_detect('Pro_B'), 'Pro-B', 'NA'))) %>% 
    ggplot(aes(x = Age, y = value, color = `Developmental State`)) + 
    geom_smooth(method = 'loess', se = T, size = 2) + #geom_point(size = 0.3, alpha = 0.5) + 
    scale_x_sqrt(breaks = c(1, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80)) + 
    scale_color_brewer(palette = 'Dark2') + 
    geom_hline(yintercept = 0, lty = 3) + 
    geom_vline(xintercept = 1, lty = 3) + geom_vline(xintercept = 18, lty = 3) + geom_vline(xintercept = 40, lty = 3) + 
    xlab('Age at Diagnosis (Years)') + ylab('B-ALL Multipotency Score') +
    ggpubr::theme_pubr(legend = 'top') + theme(legend.title = element_blank())
Error in `select()`:
! Can't subset columns that don't exist.
✖ Column `Multipotency_Score` doesn't exist.
Backtrace:
 1. ... %>% ...
 6. dplyr:::select.data.frame(., Age, Multipotency_Score)

Evaluate on MRD

# Formatted and pivoted
bulk2046_MRD <- bulk2046 %>% 
  select(Patient, MRD_D29_2cat, MRD_D29_3cat, MRD_D29_5cat,  MRD_D46_2cat, 
         c('HSC_MPP', 'Early_Lymphoid', 'Myeloid_Prog', 'Pre_pDC', 'Pro_B', 'Pre_B', 'Mature_B', 'T_NK', 'Monocyte', 'Erythroid',
           'PC1', 'Multipotency_Score')) %>% 
  mutate(MRD_D29_2cat = factor(MRD_D29_2cat, levels = c('Negative\n< 0.01%', 'Positive\n> 0.01%')), 
         MRD_D29_3cat = factor(MRD_D29_3cat, levels = c('< 0.01%', '0.01 - 1%',  '> 1%')), 
         MRD_D29_5cat = factor(MRD_D29_5cat, levels = c('< 0.01%', '0.01 - 0.1%', '0.1 - 1%', '1 - 10%', '> 10%'))) %>% 
  pivot_longer(-c(Patient, MRD_D29_2cat, MRD_D29_3cat, MRD_D29_5cat,  MRD_D46_2cat), names_to = 'Lineage', values_to = 'Score') %>% 
  select(Patient, Lineage, Score, everything()) %>% 
  mutate(Lineage = Lineage %>% #str_replace('NMF.*_', '') %>% 
                factor(levels = c('Multipotency_Score', 'PC1', 'HSC_MPP', 'Early_Lymphoid', 'Myeloid_Prog', 'Pre_pDC', 'Pro_B', 'Pre_B', 
                                  'Mature_B', 'T_NK', 'Monocyte', 'Erythroid'))) %>% 
  arrange(Lineage)

bulk2046_MRD
p <- bulk2046_MRD %>% 
    filter(MRD_D29_5cat != 'NA') %>% #mutate(Score = ifelse(Lineage %in% c('PC1_a', 'PC1_b', 'PC1_c'), -Score, Score)) %>% 
    filter(Lineage %in% c('Multipotency_Score')) %>% 
    mutate(Lineage = Lineage %>% str_replace('_', ' ')) %>% 
    ggplot(aes(x = MRD_D29_5cat, y = Score, fill = MRD_D29_5cat)) + 
    geom_hline(yintercept = 0, alpha = 0.7, lty = 2) + 
    geom_boxplot(outlier.size=0) + ggbeeswarm::geom_quasirandom(aes(size = MRD_D29_5cat), width=0.3) + 
    facet_wrap(.~Lineage, scales='free', ncol=5) + 
    theme_pubr(legend = 'none') + 
    scale_fill_manual(values = c('#7CA987', '#D9D17D', '#DAA15E', '#FF7F50', '#D43F00')) + 
    theme(strip.text.x = element_text(size = 14), axis.text.x = element_text(size = 11), axis.text.y = element_text(size = 12), axis.title = element_text(size = 12.5)) + 
    scale_size_manual(values = c(0.15, 0.4, 0.4, 0.5, 0.5)) + xlab('\nResidual Disease - Day 29 Induction') + ylab('B-ALL Multipotency Score') + 
    stat_compare_means(comparisons = list(c('< 0.01%', '0.01 - 0.1%'), c('< 0.01%', '0.1 - 1%'), c('< 0.01%', '1 - 10%'), c('< 0.01%', '> 10%')))

p
ggsave('BALL_MultipotencyScore_Figures/MRD_levels_794patients_Multipotency_Score.pdf', device = 'pdf', height = 5.5, width = 4.8)

p <- bulk2046_MRD %>% 
    filter(MRD_D29_2cat != 'NA') %>% 
    filter(Lineage %in% c('Multipotency_Score')) %>% 
    mutate(Lineage = Lineage %>% str_replace('_', ' ')) %>% 
    ggplot(aes(x = MRD_D29_2cat, y = Score, fill = MRD_D29_2cat)) + 
    geom_hline(yintercept = 0, alpha = 0.7, lty = 2) + 
    geom_boxplot(outlier.size=0) + ggbeeswarm::geom_quasirandom(size = 0.25, width=0.3) + 
    facet_wrap(.~Lineage, scales='free', ncol=5) + 
    theme_pubr(legend = 'none') + 
    scale_fill_manual(values = c('#7CA987', '#D97A6D')) + 
    theme(strip.text.x = element_text(size = 13.5), axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12), axis.title = element_text(size = 12.5)) + 
    xlab('\nResidual Disease - Day 29 Induction') + ylab('B-ALL Multipotency Score') + 
    stat_compare_means(comparisons = list(c('Negative\n< 0.01%', 'Positive\n> 0.01%')))

p
ggsave('BALL_MultipotencyScore_Figures/MRD_Status_1197patients_Multipotency_Score.pdf', device = 'pdf', height = 5, width = 4)

Evaluate on Survival Outcomes

Pediatric Survival

First in Pediatric B-ALL Survival outcomes are available for 1,039 pediatric B-ALL patients within the St Jude and COG cohorts. Note that the COG patient samples subject for RNA-seq were preselected to be high risk and consequentially these COG outcomes are worse than the “real world”

# Keep pediatric cohorts (COG and St Jude)
bulk2046_pediatric <- bulk2046 %>% filter(Institute %in% c('St Jude', 'COG')) %>%
    filter(!is.na(oscensor) & !is.na(efscensor)) %>% 
    mutate(Risk_Group = factor(Risk_Group, levels = c('Childhood SR', 'Childhood HR', 'AYA')),  # clinical risk group
           GenomicRisk_pediatric = factor(GenomicRisk_pediatric, levels = c('Unclassified', 'Favorable', 'Intermediate', 'Unfavorable')))     # genomic risk group

bulk2046_pediatric

Loop through developmental states and perform univariable + multivariable cox regression

# dataframe to store results
lineage_survival_ped <- data.frame()
devstates <- c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B', 'Mature_B', 'T_NK', 'Monocyte', 'Erythroid')

for(lineage in devstates){

    # Univariable model 
    mod_os <- coxph(Surv(ostime, oscensor) ~ get(lineage), bulk2046_pediatric)
    mod_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage), bulk2046_pediatric)

    lineage_survival_ped <- lineage_survival_ped %>% bind_rows(
        summary(mod_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    ) %>% bind_rows(
        summary(mod_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_efs$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    )
    
    # Multivariable model 
    mod_multi_os <- coxph(Surv(ostime, oscensor) ~ get(lineage) + Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC, bulk2046_pediatric)
    mod_multi_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage) + Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC, bulk2046_pediatric)
    
    # Create baseline models without dev state abundance to compare against by nested LRT
    mod_multi_os_0 <- coxph(Surv(ostime, oscensor) ~ Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC, bulk2046_pediatric)
    mod_multi_efs_0 <- coxph(Surv(efstime, efscensor) ~ Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC , bulk2046_pediatric)

    lineage_survival_ped <- lineage_survival_ped %>% bind_rows(
        summary(mod_multi_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_os, mod_multi_os_0)[2,'P(>|Chi|)'])
        
    ) %>% bind_rows(
      
        summary(mod_multi_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_efs$n) %>%
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_efs, mod_multi_efs_0)[2,'P(>|Chi|)'])
        
    )
}

lineage_survival_ped 
surv_ped_uni <- lineage_survival_ped %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == FALSE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% filter(Lineage != 'NA') %>%
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', 'darkgreen', '#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0.3,1.8)) + theme(axis.title.x= element_blank()) + 
    ylab('Univariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Univariable Survival (n = 1039)')
    
surv_ped_multi <- lineage_survival_ped %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue_nestedLRT < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue_nestedLRT < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == TRUE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score','HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% filter(Lineage != 'NA') %>%
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', 'darkgreen', '#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0.3,1.8)) + theme(axis.title.x= element_blank()) + 
    ylab('Multivariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Multivariable Survival (n = 1010)')
    
ggsave(plot = surv_ped_uni, filename = 'BALL_survival_figures_final/Survival_pediatric_Univariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)
ggsave(plot = surv_ped_multi, filename = 'BALL_survival_figures_final/Survival_pediatric_Multivariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)

surv_ped_uni | surv_ped_multi

bulk2046_pediatric$MRD_D29_2cat %>% table()
.
Negative\n< 0.01% Positive\n> 0.01% 
              650               265 

Pediatric Survival; MRD negative only

Repeat Pediatric Analysis within MRD negative patients

bulk2046_pediatric_MRDneg <- bulk2046_pediatric %>% filter(MRD_D29_2cat == 'Negative\n< 0.01%')

# dataframe to store results
lineage_survival_ped_MRDneg <- data.frame()
devstates <- c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B', 'Mature_B', 'T_NK', 'Monocyte', 'Erythroid')

for(lineage in devstates){

    # Multivariable model 
    mod_multi_os <- coxph(Surv(ostime, oscensor) ~ get(lineage) + Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC, bulk2046_pediatric_MRDneg)
    mod_multi_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage) + Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC , bulk2046_pediatric_MRDneg)
    
    # Create baseline models without dev state abundance to compare against by nested LRT
    mod_multi_os_0 <- coxph(Surv(ostime, oscensor) ~ Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC, bulk2046_pediatric_MRDneg)
    mod_multi_efs_0 <- coxph(Surv(efstime, efscensor) ~ Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC , bulk2046_pediatric_MRDneg)

    lineage_survival_ped_MRDneg <- lineage_survival_ped_MRDneg %>% bind_rows(
        summary(mod_multi_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_os, mod_multi_os_0)[2,'P(>|Chi|)'])
        
    ) %>% bind_rows(
      
        summary(mod_multi_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_efs$n) %>%
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_efs, mod_multi_efs_0)[2,'P(>|Chi|)'])
        
    )
}

lineage_survival_ped_MRDneg 
surv_ped_multi_MRDneg <- lineage_survival_ped_MRDneg %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue_nestedLRT < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue_nestedLRT < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == TRUE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score','HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% filter(Lineage != 'NA') %>%
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', 'darkgreen', '#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0.2,2.1)) + theme(axis.title.x= element_blank()) + 
    ylab('Multivariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Multivariable Survival\n(MRD negative patients; n = 649)')
    
#ggsave(plot = surv_ped_uni, filename = 'BALL_survival_figures_final/Survival_pediatric_Univariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)
#ggsave(plot = surv_ped_multi_MRDneg, filename = 'BALL_survival_figures_final/Survival_pediatric_Multivariable_BDevOnly_HazardRatios_MRDnegative.pdf', device = 'pdf', height = 7.5, width = 4.5)

surv_ped_multi_MRDneg

Adult Survival

324 adult B-ALL patients with survival data

# Use adult cohort ECOG
bulk2046_adult <- bulk2046 %>% 
    filter(!Institute %in% c('COG', 'St Jude')) %>% 
    filter(!is.na(oscensor) | !is.na(efscensor)) %>% 
    mutate(Risk_Group = factor(Risk_Group, levels = c('AYA', 'Adult')),  # clinical risk group
           GenomicRisk_adult = factor(GenomicRisk_adult, levels = c('Unclassified', 'Favorable', 'Intermediate', 'Unfavorable')))     # genomic risk group

bulk2046_adult

Loop through developmental states and perform univariable + multivariable cox regression

# dataframe to store results
lineage_survival_adult <- data.frame()
devstates <- c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B', 'Mature_B', 'T_NK', 'Monocyte', 'Erythroid')

for(lineage in devstates){

    # Univariable model stratifiying on institute and primary subtype
    mod_os <- coxph(Surv(ostime, oscensor) ~ get(lineage), bulk2046_adult)
    mod_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage), bulk2046_adult)

    lineage_survival_adult <- lineage_survival_adult %>% bind_rows(
        summary(mod_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    ) %>% bind_rows(
        summary(mod_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_efs$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    )
    
    # Multivariable model stratifiying on institute and primary subtype
    mod_multi_os <- coxph(Surv(ostime, oscensor) ~ get(lineage) + Risk_Group + GenomicRisk_adult + Sex + Age + WBC, bulk2046_adult)
    mod_multi_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage) + Risk_Group + GenomicRisk_adult + Sex + Age + WBC, bulk2046_adult)
    
    # Create baseline models without dev state abundance to compare against by nested LRT
    mod_multi_os_0 <- coxph(Surv(ostime, oscensor) ~ Risk_Group + GenomicRisk_adult + Sex + Age + WBC, bulk2046_adult)
    mod_multi_efs_0 <- coxph(Surv(efstime, efscensor) ~ Risk_Group + GenomicRisk_adult + Sex + Age + WBC, bulk2046_adult)

    lineage_survival_adult <- lineage_survival_adult %>% bind_rows(
        summary(mod_multi_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_os, mod_multi_os_0)[2,'P(>|Chi|)'])
        
    ) %>% bind_rows(
      
        summary(mod_multi_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_efs$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_efs, mod_multi_efs_0)[2,'P(>|Chi|)'])
        
    )
}

lineage_survival_adult
surv_adult_uni <- lineage_survival_adult %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == FALSE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'PC1', 'PC1-new', 'HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% filter(Lineage != 'NA') %>%
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', 'darkgreen', '#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0.5,1.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Univariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Univariable - Adult B-ALL (n = 324)')
    
surv_adult_multi <- lineage_survival_adult %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue_nestedLRT < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue_nestedLRT < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == TRUE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'PC1', 'PC1-new', 'HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% filter(Lineage != 'NA') %>%
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0.5,1.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Multivariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Multivariable - Adult B-ALL (n = 312)')
    
#ggsave(plot = surv_adult_uni, filename = 'BALL_survival_figures_final/Survival_adult_Univariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)
#ggsave(plot = surv_adult_multi, filename = 'BALL_survival_figures_final/Survival_adult_Multivariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)

surv_adult_uni | surv_adult_multi

Re-run within BCR::ABL1 patients

# Keep BCR::ABL1 patients
bulk2046_ph <- bulk2046 %>% filter(new_Subtype == 'BCR::ABL1')  

bulk2046_ph
bulk2046_ph %>% filter(oscensor %in% c('0','1')) %>% pull(Institute) %>% table()
.
     CALGB        COG ECOG-ACRIN      MDACC    St Jude       SWOG    Toronto 
        21         26          1          8         21          1          1 

Loop through developmental states and perform univariable + multivariable cox regression

# dataframe to store results
lineage_survival_ph <- data.frame()
devstates <- c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B')

for(lineage in devstates){

    # Univariable model stratifiying on institute and primary subtype
    mod_os <- coxph(Surv(ostime, oscensor) ~ get(lineage), bulk2046_ph)
    mod_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage), bulk2046_ph)

    lineage_survival_ph <- lineage_survival_ph %>% bind_rows(
        summary(mod_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    ) %>% bind_rows(
        summary(mod_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_efs$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    )
    
    # Multivariable model stratifiying on institute and primary subtype
    mod_multi_os <- coxph(Surv(ostime, oscensor) ~ get(lineage) + Risk_Group + Sex + Age + WBC, bulk2046_ph)
    mod_multi_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage) + Risk_Group + Sex + Age + WBC, bulk2046_ph)
    
    # Create baseline models without dev state abundance to compare against by nested LRT
    mod_multi_os_0 <- coxph(Surv(ostime, oscensor) ~ Risk_Group + Sex + Age + WBC, bulk2046_ph)
    mod_multi_efs_0 <- coxph(Surv(efstime, efscensor) ~ Risk_Group + Sex + Age + WBC, bulk2046_ph)

    lineage_survival_ph <- lineage_survival_ph %>% bind_rows(
        summary(mod_multi_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_os, mod_multi_os_0)[2,'P(>|Chi|)'])
        
    ) %>% bind_rows(
      
        summary(mod_multi_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_efs$n) %>%
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_efs, mod_multi_efs_0)[2,'P(>|Chi|)'])
        
    )
}

lineage_survival_ph

Make forest plots

surv_ph_uni <- lineage_survival_ph %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == FALSE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'HSC/MPP', 'Myeloid Prog', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>%  
                               #'Mature B', 'T/NK', 'Monocyte', 'Erythroid', 'Multipotency Score'))) %>% 
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', '#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0,2.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Univariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Univariable Survival (n = 79)')
    
surv_ph_multi <- lineage_survival_ph %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue_nestedLRT < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue_nestedLRT < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == TRUE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'HSC/MPP', 'Myeloid Prog', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>%  
                               #'Mature B', 'T/NK', 'Monocyte', 'Erythroid', 'Multipotency Score'))) %>% 
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0,2.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Multivariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Multivariable Survival (n = 76)')

ggsave(plot = surv_ph_uni, filename = 'BALL_survival_figures_final/Survival_Ph_BALL2046_Univariable_BDevOnly_HazardRatios.pdf',
     device = 'pdf', height = 7.5, width = 5.2)
ggsave(plot = surv_ph_multi, filename = 'BALL_survival_figures_final/Survival_Ph_BALL2046_Multivariable_BDevOnly_HazardRatios.pdf',
     device = 'pdf', height = 7.5, width = 5.2)

surv_ph_uni | surv_ph_multi

Kim et al BCR::ABL1

kim_phALL_vst <- read_csv('../subtype_subcluster/Kim2023_Ph_BALL/Kim2023_Ph_BALL_RNAseq_vst.csv')

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  Gene = col_character()
)
ℹ Use `spec()` for the full column specifications.
# calculate NMF scores from vst-normalized data
kim_phALL_vst_DevStatescores <- calculate_DevState_scores(kim_phALL_vst %>% column_to_rownames('Gene') %>% data.matrix() , modelweights_withMultipotency, scale = T, sampleID = 'Sample')
[1] "Warning: 7 genes from Dev State models are missing from query dataset"
kim_phALL_vst_DevStatescores[1:20, ]
ph_BALL_combined <- read_csv('../subtype_subcluster/Kim2023_Ph_BALL/Kim2023_Ph_BALL_anno_cleaned.csv') %>% 
  mutate(Sample = ifelse(Manuscript_name %>% str_detect('-R'), paste0(JAMLR, '_nn_M'), paste0(JAMLR, '_nn_P'))) %>% 
  inner_join(kim_phALL_vst_DevStatescores) 

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_character(),
  Cohort = col_double(),
  Age_at_dx = col_double(),
  Age_at_dx_rounded = col_double(),
  OS = col_double(),
  alive = col_double(),
  RFS = col_double(),
  relapse_or_death = col_double(),
  OS_BMT_censored = col_double(),
  alive_BMT_censored = col_double(),
  WBC = col_double()
)
ℹ Use `spec()` for the full column specifications.
Joining with `by = join_by(Sample)`
ph_BALL_combined %>% write_csv('Kim2023_Ph_BALL_DevState_Scored_May2024.csv')
ph_BALL_combined

Format for survival

ph_BALL_input <- ph_BALL_combined %>% 
  # exclude a diagnosis - relapse pair
  filter(Survival_analysis == 'yes') %>% filter(Age_at_dx > 19) 

ph_BALL_input
lineage_survival_Ph_Kim <- data.frame()
devstates <- c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B')

for(lineage in devstates){

    # Univariable model stratifiying on institute and primary subtype
    mod_os <- coxph(Surv(OS, alive) ~ get(lineage), ph_BALL_input)
    mod_rfs <- coxph(Surv(RFS, relapse_or_death) ~ get(lineage), ph_BALL_input)

    lineage_survival_Ph_Kim <- lineage_survival_Ph_Kim %>% bind_rows(
        summary(mod_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'OS') %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, statistic, pvalue)
    ) %>% bind_rows(
        summary(mod_rfs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'RFS') %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, statistic, pvalue)
    )
        
    # Multivariable model stratifiying on institute and primary subtype
    mod_multi_os <- coxph(Surv(OS, alive) ~ get(lineage) + sex + Age_at_dx + WBC, ph_BALL_input)
    mod_multi_efs <- coxph(Surv(RFS, relapse_or_death) ~ get(lineage) + sex + Age_at_dx + WBC, ph_BALL_input)
    
    # Create baseline models without dev state abundance to compare against by nested LRT
    mod_multi_os_0 <- coxph(Surv(OS, alive) ~ sex + Age_at_dx + WBC, ph_BALL_input)
    mod_multi_efs_0 <- coxph(Surv(RFS, relapse_or_death) ~ sex + Age_at_dx + WBC, ph_BALL_input)

    lineage_survival_Ph_Kim <- lineage_survival_Ph_Kim %>% bind_rows(
        summary(mod_multi_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_os, mod_multi_os_0)[2,'P(>|Chi|)'])
        
    ) %>% bind_rows(
      
        summary(mod_multi_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'RFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse) %>%
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_efs, mod_multi_efs_0)[2,'P(>|Chi|)'])
        
    )
}

lineage_survival_Ph_Kim
lineage_survival_Ph_Kim %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse) %>%
    mutate(Association = ifelse(HR_lwr > 1 & pvalue < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == FALSE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'RFS', 'Relapse-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Relapse-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('PC1_','') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% 
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', 'darkgreen', '#666666')) +   #'#744F80', '#303030'
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0,2.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Univariable Hazard Ratio  (per 1SD increase in abundance)') + #ylim(c(0, 2.5)) +
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('BCR::ABL1 Adult B-ALL (n = 41)')

    
ggsave(filename = 'BALL_survival_figures_final/Survival_Ph_Kim2023_Univariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)
lineage_survival_Ph_Kim %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse) %>%
    mutate(Association = ifelse(HR_lwr > 1 & pvalue_nestedLRT < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue_nestedLRT < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == TRUE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'RFS', 'Relapse-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Relapse-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('PC1_','') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% 
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', '#666666')) +   #'#744F80', '#303030'
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0,2.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Multivariable Hazard Ratio  (per 1SD increase in abundance)') + #ylim(c(0, 2.5)) +
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('BCR::ABL1 Adult B-ALL (n = 41)')

    
ggsave(filename = 'BALL_survival_figures_final/Survival_Ph_Kim2023_Multivariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)

Compare with MPAL

Score in B-ALL vs MPAL(B/M)

panleucohort <- readRDS("../../../../../../../../Other/PanLeuGeneExpr/panLeuTotal-rlog.rds")
panleucohort_anno <- data.table::fread('../../../../../../../../Other/PanLeuGeneExpr/PanLeuCohort.csv') 
panleucohort_BALL_MPAL <- panleucohort[,colnames(panleucohort) %in% filter(panleucohort_anno, disease %in% c('B-ALL', 'MPAL'))$sample]
panleucohort_BALL_MPAL %>% dim()
[1] 19032  1319
panleucohort_rlog_DevStatescores <- calculate_DevState_scores(panleucohort_BALL_MPAL, modelweights_withMultipotency, scale = T, sampleID = 'sample')
[1] "Warning: 12 genes from Dev State models are missing from query dataset"
panleucohort_rlog_DevStatescores
panleu_scored <- panleucohort_rlog_DevStatescores %>% left_join(panleucohort_anno)
Joining with `by = join_by(sample)`
panleu_scored %>% write_csv('panleucohort_BALL_MPAL_rlog_DevState_scores_May2024.csv')
panleu_scored

Sanity check between datasets

Check correlation between the two datasets - same samples but presumably different alignments and different normalizations.

panleu_scored %>% filter(disease == 'B-ALL') %>% 
  select(sample, HSC_MPP, Myeloid_Prog, Pre_pDC, Early_Lymphoid, Pro_B, Pre_B, Multipotency_Score) %>% 
  pivot_longer(-sample, values_to = 'rlog') %>% 
  # standardize within B-ALL
  group_by(name) %>% mutate(rlog = (rlog - mean(rlog)) / sd(rlog)) %>% 
  inner_join(
    bulk2046 %>% select(sample = SampleID_old, HSC_MPP, Myeloid_Prog, Pre_pDC, Early_Lymphoid, Pro_B, Pre_B, Multipotency_Score) %>% 
      pivot_longer(-sample, values_to = 'vst')
  ) %>% 
  mutate(name = factor(name, levels = c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B'))) %>%
  ggplot(aes(x = rlog, y = vst)) + 
  geom_hline(yintercept=0) + geom_vline(xintercept=0) + 
  geom_point(size = 0.5) + geom_smooth(method = 'lm') + stat_cor() + 
  facet_wrap(.~name, ncol = 4) + xlab('rlog normalization (Montefiori et al)') + ylab('vst normalization (B-ALL 2046 cohort)')
Joining with `by = join_by(sample, name)`

Compare Early subsets vs MPAL

pangeneleu_compare <- panleu_scored %>%
  mutate(case_ID = sample %>% str_replace('_.*','')) %>% 
  left_join(read_csv('../subtype_subcluster/Alexander_MPAL_Fusions.csv') %>%
              mutate(ZNF384r = ifelse((gene_a == 'ZNF384') | (gene_b == 'ZNF384'), 1, 0), 
                     KMT2Ar = ifelse((gene_a == 'KMT2A') | (gene_b == 'KMT2A'), 1, 0)) %>% 
              group_by(case_ID) %>% 
              summarise(ZNF384r = ifelse(sum(ZNF384r) >= 1, 'MPAL(ZNF384r)', 'Other'), 
                        KMT2Ar = ifelse(sum(KMT2Ar) >= 1, 'KMT2Ar', 'Other')) %>% unique() ) %>% 
  mutate(disease_subtype = ifelse(is.na(ZNF384r), subtype, ifelse(ZNF384r == 'MPAL(ZNF384r)', ZNF384r, subtype))) %>% 
  left_join( 
    bind_rows(
      read_csv('../subtype_subcluster/KMT2A_subcluster142.csv') %>% mutate(case_ID = Patient %>% str_replace('_.*','')) %>% select(case_ID, subset = Subgroup),
      read_tsv('../subtype_subcluster/DUX4_bulk.txt', col_names=F) %>% select(case_ID = X1, subset = X2))
    ) %>% 
  left_join(
    read_tsv('../survival_analysis_old/Ph_BALL_CellofOrigin_Subtype.tsv') %>% select(sample = Samples, Cluster)
  ) %>% 
  mutate(subset = ifelse(is.na(subset), Cluster, subset))
Warning: Missing column names filled in: 'X9' [9], 'X10' [10], 'X11' [11], 'X12' [12]
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  case_ID = col_character(),
  gene_a = col_character(),
  refseq_a = col_character(),
  `chromosome:position:strand_gene_a` = col_character(),
  gene_b = col_character(),
  refseq_b = col_character(),
  `chromosome:position:strand_gene_b` = col_character(),
  `ALAL subtype` = col_character(),
  X9 = col_logical(),
  X10 = col_logical(),
  X11 = col_logical(),
  X12 = col_logical()
)
Joining with `by = join_by(case_ID)`
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  Patient = col_character(),
  Subtype = col_character(),
  Subgroup = col_character(),
  Subgroup_Name = col_character()
)

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  X1 = col_character(),
  X2 = col_character()
)
Joining with `by = join_by(case_ID)`
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  Samples = col_character(),
  Cluster = col_character()
)
Joining with `by = join_by(sample)`
pangeneleu_compare %>% write_csv("pangeneleu_compare_BALL_MPAL_DevState_May2024.csv")
pangeneleu_compare$subset %>% table()
.
          D1           D2      KMT2A-a      KMT2A-b Ph_Early-Pro Ph_Inter-Pro  Ph_Late-Pro 
          35           48           10           69           50           38           57 
pangeneleu_compare <- read_csv("pangeneleu_compare_BALL_MPAL_DevState_May2024.csv")

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_character(),
  HSC_MPP = col_double(),
  Myeloid_Prog = col_double(),
  Pre_pDC = col_double(),
  Early_Lymphoid = col_double(),
  Pro_B = col_double(),
  Pre_B = col_double(),
  Mature_B = col_double(),
  Erythroid = col_double(),
  Monocyte = col_double(),
  T_NK = col_double(),
  Multipotency_Score = col_double(),
  ZNF384r = col_logical(),
  KMT2Ar = col_logical()
)
ℹ Use `spec()` for the full column specifications.
Warning: 110 parsing failures.
 row     col           expected actual                                                file
1229 ZNF384r 1/0/T/F/TRUE/FALSE  Other 'pangeneleu_compare_BALL_MPAL_DevState_May2024.csv'
1229 KMT2Ar  1/0/T/F/TRUE/FALSE  Other 'pangeneleu_compare_BALL_MPAL_DevState_May2024.csv'
1230 ZNF384r 1/0/T/F/TRUE/FALSE  Other 'pangeneleu_compare_BALL_MPAL_DevState_May2024.csv'
1230 KMT2Ar  1/0/T/F/TRUE/FALSE  Other 'pangeneleu_compare_BALL_MPAL_DevState_May2024.csv'
1232 ZNF384r 1/0/T/F/TRUE/FALSE  Other 'pangeneleu_compare_BALL_MPAL_DevState_May2024.csv'
.... ....... .................. ...... ...................................................
See problems(...) for more details.
pangeneleu_compare %>% select(disease_subtype, subset) %>% table()
                   subset
disease_subtype     D1 D2 KMT2A-a KMT2A-b Ph_Early-Pro Ph_Inter-Pro Ph_Late-Pro
  B-other            0  0       0       0            0            0           0
  BCL11B             0  0       0       0            0            0           0
  BCL2/MYC           0  0       0       0            0            0           0
  DDX3X-MLLT10       0  0       0       0            0            0           0
  DUX4              35 48       0       0            0            0           0
  ETV6-RUNX1         0  0       0       0            0            0           0
  HLF                0  0       0       0            0            0           0
  HOXA               0  0       0       0            0            0           0
  Hyperdiploid       0  0       0       0            0            0           0
  iAMP21             0  0       0       0            0            0           0
  IKZF1 N159Y        0  0       0       0            0            0           0
  KMT2A              0  0      10      69            0            0           0
  LowHypo            0  0       0       0            0            0           0
  MEF2D              0  0       0       0            0            0           0
  MPAL(AUL)          0  0       0       0            0            0           0
  MPAL(B/M)          0  0       0       0            0            0           0
  MPAL(KMT2Ar)       0  0       0       0            0            0           0
  MPAL(NOS (T/B))    0  0       0       0            0            0           0
  MPAL(NOS (T/B/M))  0  0       0       0            0            0           0
  MPAL(Ph)           0  0       0       0            0            0           0
  MPAL(T/M)          0  0       0       0            0            0           0
  MPAL(ZNF384r)      0  0       0       0            0            0           0
  NUTM1              0  0       0       0            0            0           0
  PAX5 P80R          0  0       0       0            0            0           0
  PAX5alt            0  0       0       0            0            0           0
  Ph                 0  0       0       0           50           38          57
  PICALM-MLLT10      0  0       0       0            0            0           0
  SET-NUP214         0  0       0       0            0            0           0
  TCF3-PBX1          0  0       0       0            0            0           0
  TLX3               0  0       0       0            0            0           0
  Y                  0  0       0       0            0            0           0
  ZEB2/CEBPE         0  0       0       0            0            0           0
  ZNF384             0  0       0       0            0            0           0
# Subset to include B-ALL and B-lineage MPALs
pangeneleu_compare <- pangeneleu_compare %>% filter(disease2 %in% c('B-ALL', 'MPAL(AUL)', 'MPAL(B/M)', 'MPAL(KMT2Ar)', 'MPAL(Ph)')) 
abundances <- c('Multipotency_Score', 'HSC_MPP', 'Early_Lymphoid', 'Myeloid_Prog', 'Pre_pDC', 'Pro_B', 'Pre_B')


# Get categories
MPAL_BALL_categories <- bind_rows(
  # ZNF384
  pangeneleu_compare %>% filter(disease_subtype %>% str_detect('ZNF384')) %>% 
    mutate(disease_category = paste0('ZNF384 ', disease)) %>% 
    select(disease_category, abundances, disease, disease2, subset), 
  # BCR::ABL1
  pangeneleu_compare %>% filter(disease_subtype %>% str_detect('Ph')) %>% 
    mutate(disease_category = paste0('BCR::ABL1 ', ifelse(disease == 'MPAL', 'MPAL',
                                                   ifelse(subset %>% str_detect('Early'), 'Early-Pro', 
                                                          ifelse(subset %>% str_detect('Inter'), 'Inter-Pro',
                                                                 ifelse(subset %>% str_detect('Late'), 'Late-Pro', 'NA')))))) %>% 
    select(disease_category, abundances, disease, disease2, subset) %>% filter(disease_category != 'BCR::ABL1 NA'), 
  # KMT2A
  pangeneleu_compare %>% filter(disease_subtype %>% str_detect('KMT2A')) %>% 
    mutate(disease_category = paste0('KMT2A ', ifelse(disease == 'MPAL', 'MPAL',
                                                      ifelse(subset %>% str_detect('KMT2A-b'), 'Early', 
                                                             ifelse(subset %>% str_detect('KMT2A-a'), 'Committed', 'NA'))))) %>% 
    select(disease_category, abundances, disease, disease2, subset) %>% filter(disease_category != 'KMT2A NA'), 
  # DUX4
  pangeneleu_compare %>% filter(disease_subtype %>% str_detect('DUX4')) %>% 
    mutate(disease_category = ifelse(subset == 'D1', 'DUX4 Committed', ifelse(subset == 'D2', 'DUX4 Early', 'NA'))) %>% 
    select(disease_category, abundances, disease, disease2) %>% filter(disease_category != 'NA'), 
  # Other 
  pangeneleu_compare %>% filter(!disease_subtype %>% str_detect('ZNF384|Ph|KMT2A|DUX4')) %>%
    mutate(disease_category = paste0('Other ', disease)) %>% 
    select(disease_category, abundances, disease, disease2, subset) %>% filter(disease_category != 'NA'), 
  ) 

MPAL_BALL_categories %>% select(disease_category, disease) %>% table()
                     disease
disease_category      B-ALL MPAL
  BCR::ABL1 Early-Pro    50    0
  BCR::ABL1 Inter-Pro    38    0
  BCR::ABL1 Late-Pro     57    0
  BCR::ABL1 MPAL          0    4
  DUX4 Committed         35    0
  DUX4 Early             48    0
  KMT2A Committed        10    0
  KMT2A Early            69    0
  KMT2A MPAL              0   13
  Other B-ALL           793    0
  Other MPAL              0   36
  ZNF384 B-ALL           53    0
  ZNF384 MPAL             0   13
cp_multipotency <- cutpointr(MPAL_BALL_categories, x = Multipotency_Score, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
Assuming the positive class is MPAL
Assuming the positive class has higher x values
plot(cp_multipotency)

cutoff <- cp_multipotency$optimal_cutpoint

p <- MPAL_BALL_categories %>% 
   mutate(disease_category = factor(disease_category, levels = rev(c('ZNF384 MPAL', 'ZNF384 B-ALL', 'KMT2A MPAL', 'KMT2A Early', 'BCR::ABL1 MPAL', 'BCR::ABL1 Early-Pro', 
                                                                'Other MPAL', 'DUX4 Early', 'BCR::ABL1 Inter-Pro', 
                                                                'BCR::ABL1 Late-Pro', 'KMT2A Committed', 'DUX4 Committed', 'Other B-ALL')))) %>%
  ggplot(aes(y = disease_category, x = Multipotency_Score, fill=stat(x))) +
    geom_density_ridges_gradient(
        jittered_points = TRUE, scale = 1.7,
        position = position_points_jitter(width = 0, height = 0), 
        point_shape = '|', point_size = 3, point_alpha = 0.3) +
    scale_fill_gradient2(midpoint=cutoff, high='#71305D', low='#5083A2', name = 'Multipotency\nScore') +
    xlab(paste0('Multipotency Score')) +
    theme_pubr() + xlim(-3, 3.7) +
    geom_vline(xintercept = cutoff, lty = 2, alpha=0.5) + 
    ylab('') + 
    theme(legend.position='right', 
          axis.text.y = element_text(size=12, color = c('dodgerblue4', 'dodgerblue4', 'dodgerblue4', 'dodgerblue4', 'dodgerblue4', 'darkgreen', 'indianred4', 
                                                        'darkgreen', 'indianred4', 'darkgreen', 'indianred4', 'darkgreen', 'indianred4')),
          axis.title.x = element_text(size=13)) 
Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
p

# need MPAL_BALL_categories, Ph, KMT2A, ZNF384
plot_BALL_MPAL_categories <- function(value = 'Multipotency_Score', value_name = 'B-ALL Multipotency Score', cutpoint = cutpoint, ylimits = c(-3, 4)){

  p0 <- MPAL_BALL_categories %>% 
    filter(disease %in% c('B-ALL', 'MPAL')) %>% filter(disease_category %>% str_detect('Other')) %>% 
    mutate(category = 'Other Subtypes') %>% mutate(disease_cat = ifelse(disease == 'MPAL', 'Other\nMPAL', 'Other\nB-ALL') %>% factor(levels = c('Other\nMPAL', 'Other\nB-ALL'))) %>% 
    select(category, disease_cat, value) %>% pivot_longer(-c(category, disease_cat)) %>% 
    ggplot(aes(x = disease_cat, y = value, fill = disease_cat)) + geom_hline(yintercept = cutpoint, lty = 2, alpha = 0.7) +
    geom_boxplot(outlier.size = 0, alpha = 0.8) + ggbeeswarm::geom_quasirandom(aes(size = disease_cat), width = 0.3) + 
    scale_fill_manual(values = c('indianred4', 'darkgreen')) + 
    scale_size_manual(values = c(0.8, 0.15)) + 
    facet_wrap(.~category) + 
    stat_compare_means(comparisons = list(c('Other\nMPAL', 'Other\nB-ALL'))) + 
    theme_pubr(legend = 'none') + ylab(value_name) + xlab('\nSubgroup') + ylim(ylimits) +
    theme(strip.text.x = element_text(size = 13), axis.title = element_text(size = 13), axis.text.x = element_text(size = 12))
  
  p1 <- MPAL_BALL_categories %>% filter(disease_category %>% str_detect('BCR::ABL1')) %>% 
    mutate(disease3 = ifelse(disease2 == 'B-ALL', paste0('BCR::ABL1\n', subset %>% str_replace('Ph_','')), 'BCR::ABL1\nMPAL') %>%  
             factor(levels = rev(c('BCR::ABL1\nLate-Pro', 'BCR::ABL1\nInter-Pro', 'BCR::ABL1\nEarly-Pro', 'BCR::ABL1\nMPAL')))) %>% 
    select(disease3, value) %>% #mutate(disease3 = paste0('BCR::ABL1\n',disease3)) %>% 
    pivot_longer(-disease3) %>% 
    mutate(category = 'BCR::ABL1') %>%
    mutate(`Disease Subgroup` = ifelse(disease3 %>% str_detect('MPAL'), '\nMPAL (B/M)\n', 
                                  ifelse(disease3 %>% str_detect('Early|ZNF384'), '\nB-ALL\nEarly/Multipotent\n',
                                         ifelse(disease3 %>% str_detect('Inter'), '\nB-ALL\nInterPro\n', '\nB-ALL\nCommitted\n'))) %>% 
             factor(levels = c('\nMPAL (B/M)\n', '\nB-ALL\nEarly/Multipotent\n', '\nB-ALL\nInterPro\n', '\nB-ALL\nCommitted\n'))) %>% 
    ggplot(aes(x = disease3, y = value, fill = `Disease Subgroup`)) + geom_hline(yintercept = cutpoint, lty = 2, alpha = 0.7) +
    geom_boxplot(outlier.size = 0, alpha = 0.8) + ggbeeswarm::geom_quasirandom(aes(size = `Disease Subgroup`), width = 0.3) + 
    scale_fill_manual(values = c('indianred4', '#FF7F00', '#5EA2CF', '#1061D9')) + 
    scale_size_manual(values = c(1, 0.6, 0.7, 0.7)) + 
    facet_wrap(.~category) + 
    stat_compare_means(comparisons = list(c('BCR::ABL1\nMPAL', 'BCR::ABL1\nEarly-Pro'), 
                                          c('BCR::ABL1\nMPAL', 'BCR::ABL1\nInter-Pro'),
                                          c('BCR::ABL1\nMPAL', 'BCR::ABL1\nLate-Pro'))) + 
    theme_pubr(legend = 'none') + ylab(value_name) + xlab('\nSubgroup') + ylim(ylimits) +
    theme(strip.text.x = element_text(size = 13), axis.title = element_text(size = 13), axis.text.x = element_text(size = 12))
  
  p2 <- MPAL_BALL_categories %>% filter(disease_category %>% str_detect('KMT2A')) %>% 
    mutate(disease3 = ifelse(disease == 'B-ALL', subset, ifelse(disease == 'MPAL', 'KMT2A-r\nMPAL', subset))) %>% 
    mutate(disease3 = disease3 %>% str_replace('KMT2A-a', 'KMT2A-r\nCommitted') %>% str_replace('KMT2A-b', 'KMT2A-r\nEarly') %>% 
             factor(levels = c('KMT2A-r\nMPAL', 'KMT2A-r\nEarly', 'KMT2A-r\nCommitted'))) %>% filter(disease3 != 'NA') %>% 
    select(disease3, value) %>% #mutate(disease3 = paste0('BCR::ABL1\n',disease3)) %>% 
    pivot_longer(-disease3) %>% 
    mutate(category = 'KMT2A-r') %>%
    mutate(`Disease Subgroup` = ifelse(disease3 %>% str_detect('MPAL'), '\nMPAL (B/M)\n', 
                                  ifelse(disease3 %>% str_detect('Early|ZNF384'), '\nB-ALL\nEarly/Multipotent\n', '\nB-ALL\nCommitted\n')) %>% 
             factor(levels = c('\nMPAL (B/M)\n', '\nB-ALL\nEarly/Multipotent\n', '\nB-ALL\nCommitted\n'))) %>% 
    ggplot(aes(x = disease3, y = value, fill = `Disease Subgroup`)) + geom_hline(yintercept = cutpoint, lty = 2, alpha = 0.7) +
    geom_boxplot(outlier.size = 0, alpha = 0.8) + ggbeeswarm::geom_quasirandom(aes(size = `Disease Subgroup`), width = 0.3) + 
    scale_fill_manual(values = c('indianred4', '#B22122', '#1D90FF')) + 
    scale_size_manual(values = c(1, 0.6, 0.8)) + 
    facet_wrap(.~category) + 
    stat_compare_means(comparisons = list(c('KMT2A-r\nMPAL', 'KMT2A-r\nEarly'), c('KMT2A-r\nMPAL', 'KMT2A-r\nCommitted'))) +
    theme_pubr(legend = 'none') + ylab(value_name) + xlab('\nSubgroup') + ylim(ylimits) +
    theme(strip.text.x = element_text(size = 13), axis.title = element_text(size = 13), axis.text.x = element_text(size = 12))
  
  
  p3 <- MPAL_BALL_categories %>% filter(disease_category %>% str_detect('ZNF384')) %>% 
    mutate(disease3 = paste0('ZNF384-r\n',disease) %>% factor(levels = c('ZNF384-r\nMPAL', 'ZNF384-r\nB-ALL'))) %>% select(disease3, value) %>% 
    pivot_longer(-disease3) %>% 
    mutate(category = 'ZNF384-r') %>%
    mutate(`Disease Subgroup` = ifelse(disease3 %>% str_detect('MPAL'), '\nMPAL (B/M)\n', 
                                  ifelse(disease3 %>% str_detect('Early|ZNF384'), '\nB-ALL\nEarly/Multipotent\n', '\nB-ALL\nCommitted\n')) %>% 
             factor(levels = c('\nMPAL (B/M)\n', '\nB-ALL\nEarly/Multipotent\n', '\nB-ALL\nCommitted\n'))) %>% 
    ggplot(aes(x = disease3, y = value, fill = `Disease Subgroup`)) + geom_hline(yintercept = cutpoint, lty = 2, alpha = 0.7) +
    geom_boxplot(outlier.size = 0, alpha = 0.8) + ggbeeswarm::geom_quasirandom(aes(size = `Disease Subgroup`), width = 0.3) + 
    scale_fill_manual(values = c('indianred4', 'dodgerblue4')) + 
    scale_size_manual(values = c(1, 0.7)) + 
    facet_wrap(.~category) + 
    stat_compare_means(comparisons = list(c('ZNF384-r\nMPAL', 'ZNF384-r\nB-ALL'))) + 
    theme_pubr(legend = 'none') + ylab(value_name) + xlab('\nSubgroup') + ylim(ylimits) +
    theme(strip.text.x = element_text(size = 13), axis.title = element_text(size = 13), axis.text.x = element_text(size = 12))
  
  return(ggarrange(p0, p1, p2, p3, ncol = 4, widths = c(0.58, 1, 0.8, 0.58)))
}
plot_BALL_MPAL_categories(value = 'Multipotency_Score', value_name = 'B-ALL Multipotency Score', cutpoint = cp_multipotency$optimal_cutpoint, ylimits = c(-3, 3.75))
Warning: cannot compute exact p-value with ties

ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_MultipotencyScore.pdf', height = 4.8, width = 15, device = 'pdf')
cp <- cutpointr(MPAL_BALL_categories, x = HSC_MPP, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
Assuming the positive class is MPAL
Assuming the positive class has higher x values
plot_BALL_MPAL_categories(value = 'HSC_MPP', value_name = 'HSC/MPP Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-2.8, 4))
Warning: cannot compute exact p-value with ties

ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_HSCMPP.pdf', height = 4.8, width = 15, device = 'pdf')
cp <- cutpointr(MPAL_BALL_categories, x = Early_Lymphoid, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
Assuming the positive class is MPAL
Assuming the positive class has higher x values
plot_BALL_MPAL_categories(value = 'Early_Lymphoid', value_name = 'Early Lymphoid Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-2.8, 4))
Warning: cannot compute exact p-value with ties

ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_EarlyLymphoid.pdf', height = 4.8, width = 15, device = 'pdf')
cp <- cutpointr(MPAL_BALL_categories, x = Myeloid_Prog, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
Assuming the positive class is MPAL
Assuming the positive class has higher x values
plot_BALL_MPAL_categories(value = 'Myeloid_Prog', value_name = 'Myeloid Progenitor Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-2.8, 5))
Warning: cannot compute exact p-value with ties

ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_MyeloidProg.pdf', height = 4.8, width = 15, device = 'pdf')
cp <- cutpointr(MPAL_BALL_categories, x = Pre_pDC, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
Assuming the positive class is MPAL
Assuming the positive class has higher x values
plot_BALL_MPAL_categories(value = 'Pre_pDC', value_name = 'Pre-pDC Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-2.8, 4))
Warning: cannot compute exact p-value with ties

ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_PrePDC.pdf', height = 4.8, width = 15, device = 'pdf')
cp <- cutpointr(MPAL_BALL_categories, x = Pro_B, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
Assuming the positive class is B-ALL
Assuming the positive class has higher x values
plot_BALL_MPAL_categories(value = 'Pro_B', value_name = 'Pro-B Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-3.5, 3.3))
Warning: cannot compute exact p-value with ties

ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_ProB.pdf', height = 4.8, width = 15, device = 'pdf')
cp <- cutpointr(MPAL_BALL_categories, x = Pre_B, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
Assuming the positive class is B-ALL
Assuming the positive class has higher x values
plot_BALL_MPAL_categories(value = 'Pre_B', value_name = 'Pre-B Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-3, 3.8))
Warning: cannot compute exact p-value with ties

ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_ProB.pdf', height = 4.8, width = 15, device = 'pdf')

Score on Pharmacotype Drug Screening Data

# require gene symbol column to be named "Gene"
rpkm_to_logTPM <- function(dat){
  # convert to TPM
  dat_TPM <- dat %>% 
    gather(-Gene, key = "Sample", value = "RPKM") %>%
    group_by(Sample) %>% 
    mutate(logTPM = log1p(RPKM / sum(RPKM) * 1000000)) %>% 
    select(-RPKM) %>% ungroup() %>% 
    spread(Sample, logTPM)
  
  return(dat_TPM)
}

# load pharmacotype data and convert to logTPM
pharmacotype_fpkm <- data.table::fread('../pharmacotypes/pharmacotyping_ped_rnaseq_fpkm_ALLids_0823.csv') %>% select(-GeneID) %>% dplyr::rename(Gene = GeneName)
pharmacotype_logTPM <- pharmacotype_fpkm %>% rpkm_to_logTPM()
pharmacotype_logTPM <- pharmacotype_logTPM %>% column_to_rownames('Gene') %>% data.matrix()
pharmacotype_logTPM %>% dim()
[1] 18834   712
pharmacotype_logTPM_scored <- calculate_DevState_scores(pharmacotype_logTPM, modelweights_withMultipotency, scale = T, sampleID = 'Patient ID')
[1] "Warning: 11 genes from Dev State models are missing from query dataset"
pharmacotype_logTPM_scored %>% write_csv('ALL_pharmacotypes_logTPM_DevState_scores_May2024.csv')
pharmacotype_logTPM_scored

Which version of PC1 best separates subtypes within KMT2A, DUX4, BCR::ABL1?

bulk2046_subtype_subcluster <- bulk2046 %>% filter(new_Subtype %in% c('BCR::ABL1', 'KMT2A', 'DUX4')) %>% select(Patient, PatientID = PatientID_old, new_Subtype, 
                                                                                                                Multipotency_Score, HSC_MPP, Early_Lymphoid, Pro_B, Pre_B, 
                                                                                                                Institute, oscensor, ostime, efscensor, efstime) %>% 
  left_join( read_tsv("../subtype_subcluster/Ph_sub_clusters_S2046.txt", col_names = c('PatientID', 'Class')) %>% dplyr::rename(Ph_Class = Class), by = 'PatientID') %>% 
  left_join( read_tsv("../subtype_subcluster/DUX4_bulk.txt", col_names = c('PatientID', 'Class')) %>% dplyr::rename(DUX4_Class = Class), by = 'PatientID') %>%
  left_join( read_csv("../subtype_subcluster/KMT2A_subcluster142.csv") %>% select(Patient, KMT2A_Class = Subgroup), by = 'Patient') %>% 
  arrange(new_Subtype) 

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  PatientID = col_character(),
  Class = col_character()
)
Warning: 139 parsing failures.
row col  expected    actual                                              file
  1  -- 2 columns 3 columns '../subtype_subcluster/Ph_sub_clusters_S2046.txt'
  2  -- 2 columns 3 columns '../subtype_subcluster/Ph_sub_clusters_S2046.txt'
  3  -- 2 columns 3 columns '../subtype_subcluster/Ph_sub_clusters_S2046.txt'
  4  -- 2 columns 3 columns '../subtype_subcluster/Ph_sub_clusters_S2046.txt'
  5  -- 2 columns 3 columns '../subtype_subcluster/Ph_sub_clusters_S2046.txt'
... ... ......... ......... .................................................
See problems(...) for more details.

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  PatientID = col_character(),
  Class = col_character()
)

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  Patient = col_character(),
  Subtype = col_character(),
  Subgroup = col_character(),
  Subgroup_Name = col_character()
)
bulk2046_subtype_subcluster$KMT2A_Class %>% table()
.
KMT2A-a KMT2A-b 
     17     125 
cutpointr(bulk2046_subtype_subcluster$HSC_MPP, bulk2046_subtype_subcluster$KMT2A_Class, na.rm=TRUE)
Assuming the positive class is KMT2A-b
Assuming the positive class has higher x values
cutpointr(bulk2046_subtype_subcluster$Early_Lymphoid, bulk2046_subtype_subcluster$KMT2A_Class, na.rm=TRUE)
Assuming the positive class is KMT2A-b
Assuming the positive class has higher x values
cutpointr(bulk2046_subtype_subcluster$Multipotency_Score, bulk2046_subtype_subcluster$KMT2A_Class, na.rm=TRUE)
Assuming the positive class is KMT2A-b
Assuming the positive class has higher x values
cutpointr(bulk2046_subtype_subcluster$HSC_MPP, bulk2046_subtype_subcluster$DUX4_Class, na.rm=TRUE)
Assuming the positive class is D2
Assuming the positive class has higher x values
cutpointr(bulk2046_subtype_subcluster$Early_Lymphoid, bulk2046_subtype_subcluster$DUX4_Class, na.rm=TRUE)
Assuming the positive class is D2
Assuming the positive class has higher x values
cutpointr(bulk2046_subtype_subcluster$Multipotency_Score, bulk2046_subtype_subcluster$DUX4_Class, na.rm=TRUE)
Assuming the positive class is D2
Assuming the positive class has higher x values
temp <- bulk2046_subtype_subcluster %>% filter(Ph_Class %>% str_detect("Early|Late"))

cutpointr(temp$HSC_MPP, temp$Ph_Class, na.rm=TRUE)
Assuming the positive class is Ph_Early-Pro
Assuming the positive class has higher x values
cutpointr(temp$Early_Lymphoid, temp$Ph_Class, na.rm=TRUE)
Assuming the positive class is Ph_Early-Pro
Assuming the positive class has higher x values
cutpointr(temp$Multipotency_Score, temp$Ph_Class, na.rm=TRUE)
Assuming the positive class is Ph_Early-Pro
Assuming the positive class has higher x values
temp <- bulk2046_subtype_subcluster %>% filter(Ph_Class %>% str_detect("Early|Inter"))

cutpointr(temp$HSC_MPP, temp$Ph_Class, na.rm=TRUE)
Assuming the positive class is Ph_Early-Pro
Assuming the positive class has higher x values
cutpointr(temp$Early_Lymphoid, temp$Ph_Class, na.rm=TRUE)
Assuming the positive class is Ph_Early-Pro
Assuming the positive class has higher x values
cutpointr(temp$Multipotency_Score, temp$Ph_Class, na.rm=TRUE)
Assuming the positive class is Ph_Early-Pro
Assuming the positive class has higher x values
temp <- bulk2046_subtype_subcluster %>% filter(Ph_Class %>% str_detect("Late|Inter"))

cutpointr(temp$HSC_MPP, temp$Ph_Class, na.rm=TRUE)
Assuming the positive class is Ph_Inter-Pro
Assuming the positive class has higher x values
cutpointr(temp$Early_Lymphoid, temp$Ph_Class, na.rm=TRUE)
Assuming the positive class is Ph_Inter-Pro
Assuming the positive class has higher x values
cutpointr(temp$Multipotency_Score, temp$Ph_Class, na.rm=TRUE)
Assuming the positive class is Ph_Inter-Pro
Assuming the positive class has higher x values
---
title: "B-ALL Composition Multipotency Score"
output: html_notebook
---

```{r}
library(tidyverse)
library(ggpubr)
library(survival)
library(survminer)
library(patchwork)
```

```{r}
bulk2046 <- read_csv('BALL2046_DevState_Updated_April2024_Fusions_MRD_AZ.csv')
bulk2046 
```

# PCA on Dev State Abundance
Include the four main lineages along B cell development

```{r}
LineageScores <- bulk2046 %>% select(PatientID, HSC_MPP, Early_Lymphoid, Pro_B, Pre_B) %>%   
  column_to_rownames('PatientID') %>% data.matrix()
bulk2046$PC1 <- prcomp(LineageScores)[5]$x[,1]
prcomp(LineageScores, scale=T, center=T)
```

## Principal Component 1 is a Multipotency Score 

```{r}
DevState_PCA <- data.frame(prcomp(LineageScores, scale=T, center=T)[2]$rotation) %>% rownames_to_column('DevState')
color <- ifelse(DevState_PCA$PC1 > 0, 'darkgreen', 'darkorange')


DevState_PCA %>% 
  mutate(DevState = factor(DevState %>% str_replace('HSC_MPP', 'HSC/MPP') %>% str_replace('_B','-B') %>% str_replace('_',' '), 
                           levels = rev(c("HSC/MPP", "Early Lymphoid", "Pro-B", "Pre-B")))) %>% 
  ggplot(aes(x = DevState, y = PC1)) +
  geom_bar(stat = "identity", show.legend = FALSE, fill = color, color = "white") +
  geom_hline(yintercept = 0, color = 1, lwd = 0.2) +
  geom_text(aes(label = DevState, # Text with groups
                hjust = ifelse(PC1 < 0, 1.25, -0.15),
                vjust = 0.5), size = 3.5) +
  xlab("Developmental State") + ylab("PC1 Feature Loadings") +
  scale_y_continuous(breaks = seq(-1, 1, by = 0.25), limits = c(-0.8, 0.8)) +
  coord_flip() +
  theme_minimal() +
  theme(axis.text.y = element_blank(),  # Remove Y-axis texts
        axis.ticks.y = element_blank(), # Remove Y-axis ticks
        panel.grid.major.y = element_blank(),
        panel.grid.minor.x = element_blank()) # Remove horizontal grid

ggsave('BALL_MultipotencyScore_Figures/MultipotencyScore_PC1_FeatureLoadings.pdf', height = 3.2, width=6)
```

### Derive gene signature for estimating PC1 

```{r}
train_LASSO <- function(x_train, y_train, alpha = 1){
  
  train_y <- y_train$PC1
  
  # Perform Lasso regression with LOOCV 
  model <- cv.glmnet(x = x_train, y = train_y, nfold = 10, family = 'gaussian', alpha = alpha, maxit=1000000, standardize=FALSE)
  #plot(model)

  return(model)
}

evaluate_model <- function(model, x_val, anno_val, lambda, 
                           feature_name, iteration, foldname){

  # Create score classification with survival and get covariates
  pred_y <- predict(model, x_val, s = lambda) %>% data.frame()
  colnames(pred_y) <- 'PredScore'
  pred_y <- pred_y %>% rownames_to_column('Patient') %>% 
    # add anno to get covariates
    left_join(anno_val, by = 'Patient')
  
  # Calculate correlation in validation set
  pearson <- cor(pred_y$PredScore, pred_y$PC1, method = 'pearson')
  spearman <- cor(pred_y$PredScore, pred_y$PC1, method = 'spearman')
  
  # Summary Metrics
  summary_metrics <- data.frame(
    'model_id' = paste0(feature_name, '_iter', iteration, '_', foldname),
    'lambda' = lambda,
    'model_size' = sum(coef(model, s = lambda)!=0),
    'pearson' = pearson,
    'spearman' = spearman,
    'features' = feature_name,
    'iteration' = iteration,
    'foldname' = foldname
  )
  return(summary_metrics)
}


gridsearch_lasso <- function(expr_train, expr_val, anno_train, anno_val, features, feature_name,
                             iteration, foldname, summary_metrics){
  
  # Filter expr matrix for feature set
  x_train <- expr_train[, colnames(expr_train) %in% features]
  x_val <- expr_val[, colnames(expr_val) %in% features]

  # Train LASSO 
  model <- train_LASSO(x_train, anno_train)

  # Get summary metrics for lambda.min and lambda.1se
  for(lambda in c('lambda.min', 'lambda.1se')){
    summary_metrics <- summary_metrics %>% rbind(
      evaluate_model(model = model, x_val = x_val, anno_val = anno_val, lambda = lambda, 
                     feature_name = feature_name, iteration = iteration, foldname = foldname))
  }
  
  return(summary_metrics)
}


nestedCV_regression <- function(train_anno, train_expr, iteration, feature_sets, summary_metrics){
  # set up random seed and shuffle data 
  set.seed(iteration)
  train_anno <- train_anno[sample(nrow(train_anno)),]
  train_expr <- train_expr[sample(nrow(train_expr)),]
  
  ## 10-fold outer cross validation
  folds <- rsample::vfold_cv(train_anno, 10)
  for(outer_cv in 1:10){
    # fold ID
    foldname <- folds$id[[outer_cv]]
    # get anno splits
    anno_train <- analysis(folds$splits[[outer_cv]])
    anno_val <- assessment(folds$splits[[outer_cv]])
    # get expr splits
    expr_train <- train_expr[anno_train$Patient,]
    expr_val <- train_expr[anno_val$Patient,]
    
    # Iterate through feature set and run gridsearch to train survival functions
    for(feature_name in names(feature_sets)){
      # get feature list
      features <- feature_sets[[feature_name]]
      # run gridsearch and get results
      summary_metrics <- gridsearch_lasso(expr_train = expr_train, expr_val = expr_val, anno_train = anno_train, anno_val = anno_val, 
                                  features = features, feature_name = feature_name, iteration = iteration, foldname = foldname,
                                  summary_metrics = summary_metrics)
    }
  }
  return(summary_metrics)
}

```

```{r}
bulk2046_vst <- readRDS('../BALL2046_BulkRNA_vst.rds')
bulk2046_vst %>% dim()
```

```{r}
# Train from genes used to predict the four bdev lineage populations
modelweights <- read_csv("../NMF_Lasso_ModelWeights.csv") %>% select(Gene, HSC_MPP, Early_Lymphoid, Pro_B, Pre_B) %>% 
  rowwise() %>% mutate(sumweights = sum(HSC_MPP, Early_Lymphoid, Pro_B, Pre_B)) %>% filter(sumweights != 0) %>% select(-sumweights) 

bulk2046_vst_filtered <- bulk2046_vst[modelweights$Gene,] 
bulk2046_vst_filtered %>% dim()
```

```{r}
library(tidymodels)
library(glmnet)

CVoutput <- data.frame()
train_x <- bulk2046_vst_filtered[,bulk2046$SampleID_old] %>% t()
train_y <- bulk2046 %>% select(Patient = SampleID_old, PC1)# %>% mutate(PC1 = -PC1)
featurespace <- list('ModelWeights115' = modelweights$Gene)
temp_output <- data.frame()


for(iteration in 1:10){
  print(paste0('iteration ', iteration))
  CVoutput <- nestedCV_regression(train_anno = train_y, train_expr = train_x, iteration = iteration, feature_sets = featurespace, 
                                summary_metrics = CVoutput) 
}
## annotate and add to final output
CVoutput 
CVoutput %>% write_csv('RepNestedCV_results_PC1multipotency_Regression.csv')
```

```{r}
train_LASSO <- function(x_train, y_train, alpha = 1){
  
  train_y <- y_train$PC1
  
  # Perform Lasso regression with LOOCV 
  model <- cv.glmnet(x = x_train, y = train_y, nfold = 10, family = 'gaussian', alpha = alpha, maxit=1000000, standardize=FALSE)
  #plot(model)

  return(model)
}
```


```{r}
model <- train_LASSO(train_x, y_train = train_y)

# PC1 model weights
PC1_modelweights <- data.frame()

PC1_modelweights <- model %>% coef(s = 'lambda.1se') %>% data.matrix() %>% 
      data.frame() %>% dplyr::rename(Weight = s1) %>% rownames_to_column('Gene') %>% 
      tail(-1) %>% filter(Weight != 0) %>% arrange(-Weight) 
    
PC1_modelweights <- PC1_modelweights %>% select(Gene, Weight)
PC1_modelweights
```

```{r}
# Create final model matrix
modelweights_withMultipotency <- read_csv("../NMF_Lasso_ModelWeights.csv")  %>% 
  left_join(PC1_modelweights %>% select(Gene, Multipotency_Score = Weight)) %>% 
  replace(is.na(.), 0) 

modelweights_withMultipotency %>% write_csv("DevState_Lasso_ModelWeights_withMultipotencyScore_May2024.csv")
```


```{r}
calculate_DevState_scores = function(query, modelweights, scale = TRUE, sampleID = 'Patient'){
  
  # Check for overlap with model genes and query genes
  querygenes <- rownames(query)
  modelweights_missing <- sum(!(modelweights$Gene %in% querygenes))
  # check for missing genes
  if(modelweights_missing > 0){
    print(paste0('Warning: ', modelweights_missing, ' genes from Dev State models are missing from query dataset'))
  }
  
  # filter model weights
  modelweights <- modelweights %>% filter(Gene %in% querygenes)
  modelweights_mat <- modelweights %>% column_to_rownames('Gene') %>% data.matrix()
  
  # multiply query by Dev State lasso weights
  scored <- (t(query[modelweights$Gene,]) %*% modelweights_mat) %>% data.matrix() 
  if(scale == TRUE){
    scored <- scale(scored)
  }
  scored <- scored %>% as.data.frame() %>% rownames_to_column(sampleID) 
  
  return(scored)
}
```


## Calculate in bulk2046 and validate

```{r}
bulk2046 <- bulk2046 %>% 
  left_join( calculate_DevState_scores(bulk2046_vst, modelweights_withMultipotency, scale = T, sampleID = 'SampleID_old') %>% select(SampleID_old, Multipotency_Score) ) 
bulk2046
```

```{r}
# Load repeated nested cross-validation (10-fold, 10 repeats) results 
CVoutput <- read_csv('RepNestedCV_results_PC1multipotency_Regression.csv')
PC1model_CVplot <- CVoutput %>% filter(lambda == 'lambda.1se') %>%
  mutate(model = '') %>% 
  ggplot(aes(x = model, y = pearson^2)) +
  ylab('Pearson Correlation with PC1') + xlab('PC1 Models\n(Cross-Validation)') + 
  geom_boxplot(outlier.size=0) + ggbeeswarm::geom_quasirandom(width=0.32) + theme_pubr() + theme(axis.ticks.x = element_blank())

# Get correlation across full cohort
bulk2046_PC1_Multipotency_plot <- bulk2046 %>% 
  ggplot(aes(x = Multipotency_Score, y = PC1)) + 
  xlab('B-ALL Multipotency Score (99 Genes)') + 
  geom_point(size=0.7) + stat_cor(r.digits = 4) + theme_pubr()

PC1model_CVplot + bulk2046_PC1_Multipotency_plot + plot_layout(widths=c(0.25,0.75))
ggsave('BALL_MultipotencyScore_Figures/MultipotencyScore_PC1_ModelPerformance.pdf', height = 4.5, width=8)
```


```{r}
bulk2046 %>% write_csv('BALL2046_DevState_Updated_May2024_AZ.csv')
bulk2046
```


# Multipotency Score in Normal B cell Development

```{r}
library(DESeq2)
BDev_pseudobulk <- readRDS('../../BDevelopment_Pseudobulk_byTissue_CellType.rds')
# vst normalize
BDev_pseudobulk[['RNA']]@data <- DESeqDataSetFromMatrix(BDev_pseudobulk[['RNA']]@counts, colData = BDev_pseudobulk@meta.data, design = ~1) %>% vst() %>% assay()
BDev_pseudobulk[['RNA']]@data[1:10,1:5]
```

```{r}
# Calculate Multipotency score
BDev_pseudobulk@meta.data <- bind_cols(BDev_pseudobulk@meta.data, 
                                       calculate_DevState_scores(BDev_pseudobulk[['RNA']]@data, modelweights_withMultipotency, scale = T, sampleID = 'Sample') %>% column_to_rownames('Sample'))
BDev_pseudobulk@meta.data
```

```{r}
BDev_pseudobulk@meta.data %>% 
  filter(nCells >= 50) %>% 
  mutate(BDevelopment_CellType_Comprehensive = BDevelopment_CellType_Comprehensive %>% 
           factor(levels = c('HSC/MPP', 'MPP-MyLy', 'LMPP', 'Early GMP', 'Pre-pDC', 'Pre-pDC Cycling', 'pDC', 'MLP', 
                             'CLP', 'Pre-Pro-B', 'Pro-B VDJ', 'Pro-B Cycling 1', 'Pro-B Cycling 2',  # 'Pre-Pro-B Cycling', 
                             'Large Pre-B 1', 'Large Pre-B 2', 'Small Pre-B', 'Immature B', 'Mature B'))) %>%  #Mature B Cycling
  mutate(`Developmental State` = ifelse(BDevelopment_CellType_Comprehensive %in% c('HSC/MPP', 'MPP-MyLy', 'LMPP'), 'HSC/MPP',
                                        ifelse(BDevelopment_CellType_Comprehensive %in% c('Early GMP'), 'Myeloid Progenitor',
                                               ifelse(BDevelopment_CellType_Comprehensive %in% c('Pre-pDC', 'Pre-pDC Cycling', 'pDC'), 'Pre-pDC',
                                                   ifelse(BDevelopment_CellType_Comprehensive %in% c('MLP', 'CLP', 'Pre-Pro-B', 'Pre-Pro-B Cycling'), 'Early Lymphoid',
                                                          ifelse(BDevelopment_CellType_Comprehensive %in% c('Pro-B VDJ', 'Pro-B Cycling 1', 'Pro-B Cycling 2'), 'Pro-B',
                                                                 ifelse(BDevelopment_CellType_Comprehensive %in% c('Large Pre-B 1', 'Large Pre-B 2', 'Small Pre-B'), 'Pre-B',
                                                                        ifelse(BDevelopment_CellType_Comprehensive %in% c('Immature B', 'Mature B', 'Mature B Cycling'), 'Mature B', 'NA'))))))) %>% 
           factor(levels = c('HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B', 'Mature B'))) %>% 
  filter(!is.na(BDevelopment_CellType_Comprehensive)) %>% 
  ggplot(aes(x = BDevelopment_CellType_Comprehensive, y = Multipotency_Score, fill = `Developmental State`)) + 
  geom_hline(yintercept=0, lty=5, size=0.5, alpha=0.8) + geom_boxplot(outlier.size=0.3) + 
  theme_pubr(legend='right') + theme(axis.text.x = element_text(angle=90, hjust=1, size=10.5)) + 
  xlab('\nNormal Population (B Cell Development Atlas)') + ylab('B-ALL Multipotency Score') + 
  scale_fill_brewer(palette = 'Dark2')

ggsave('BALL_MultipotencyScore_Figures/MultipotencyScore_Normal_BDev_pseudobulkScores.pdf', height = 5, width=9.5)
```

# Evaluate 99-gene Multipotency Score within 2046 cohort 

```{r}
bulk2046 <- read_csv('BALL2046_DevState_Updated_May2024_AZ.csv')
```

## Evaluate on Clinical Risk Group 

```{r}
bulk2046$Risk_Group %>% table()
```


```{r}
p <- bulk2046 %>% 
    select(Patient, Risk_Group, Multipotency_Score) %>% pivot_longer(-c(Patient, Risk_Group), names_to='Lineage', values_to='Score') %>%
    filter(Risk_Group != 'NA') %>% mutate(Risk_Group = Risk_Group %>% factor(levels = c('Childhood SR', 'Childhood HR', 'AYA', 'Adult'))) %>% 
    filter(Lineage %in% c('Multipotency_Score')) %>% 
    mutate(Lineage = Lineage %>% str_replace('_', ' ')) %>% 
    ggplot(aes(x = Risk_Group, y = Score, fill = Risk_Group)) + 
    geom_hline(yintercept = 0, alpha = 0.7, lty = 2) + 
    geom_boxplot(outlier.size=0) + ggbeeswarm::geom_quasirandom(aes(size = Risk_Group), width=0.3) + 
    facet_wrap(.~Lineage, scales='free', ncol=5) + 
    theme_pubr(legend = 'none') + 
    scale_fill_brewer(palette = 'Dark2') + 
    theme(strip.text.x = element_text(size = 14), axis.text.x = element_text(size = 11), axis.text.y = element_text(size = 12), axis.title = element_text(size = 12.5)) + 
    scale_size_manual(values = c(0.2, 0.2, 0.3, 0.3)) + xlab('\nClinical Risk Group') + ylab('B-ALL Multipotency Score') + 
    stat_compare_means(comparisons = list(c('Childhood SR', 'Childhood HR'), c('Childhood HR', 'AYA'), c('AYA', 'Adult')))

p
ggsave('BALL_MultipotencyScore_Figures/RiskGroup_2022patients_Multipotency_Score.pdf', device = 'pdf', height = 5.5, width = 4.8)
```

```{r}
# Patients with Age data
sum(!is.na(bulk2046$Age)) 
```


```{r}
p <- bulk2046 %>% select(Age, Multipotency_Score) %>% pivot_longer(-Age) %>% 
    mutate(`Developmental State` = name %>% str_replace('_',' ')) %>%
    #mutate(`Developmental State` = ifelse(name %>% str_detect('Early'), 'Early Lymphoid',
    #                                     ifelse(name %>% str_detect('Pro_B'), 'Pro-B', 'NA'))) %>% 
    ggplot(aes(x = Age, y = value, color = `Developmental State`)) + 
    geom_smooth(method = 'loess', se = T, size = 2) + #geom_point(size = 0.3, alpha = 0.5) + 
    scale_x_sqrt(breaks = c(1, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80)) + 
    scale_color_brewer(palette = 'Dark2') + 
    geom_hline(yintercept = 0, lty = 3) + 
    geom_vline(xintercept = 1, lty = 3) + geom_vline(xintercept = 15, lty = 3) + geom_vline(xintercept = 40, lty = 3) + 
    xlab('Age at Diagnosis (Years)') + ylab('B-ALL Multipotency Score') +
    ggpubr::theme_pubr(legend = 'top') + theme(legend.title = element_blank())
        
p
ggsave('BALL_MultipotencyScore_Figures/Age_vs_MultipotencyScore_2019patients.pdf', height = 4.2, width = 5.5)
```


```{r}
p <- bulk2046 %>% select(Age, Multipotency_Score) %>% pivot_longer(-Age) %>% 
    mutate(`Developmental State` = name %>% str_replace('_',' ')) %>%
    #mutate(`Developmental State` = ifelse(name %>% str_detect('Early'), 'Early Lymphoid',
    #                                     ifelse(name %>% str_detect('Pro_B'), 'Pro-B', 'NA'))) %>% 
    ggplot(aes(x = Age, y = value, color = `Developmental State`)) + 
    geom_smooth(method = 'loess', se = T, size = 2) + #geom_point(size = 0.3, alpha = 0.5) + 
    scale_x_sqrt(breaks = c(1, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80)) + 
    scale_color_brewer(palette = 'Dark2') + 
    geom_hline(yintercept = 0, lty = 3) + 
    geom_vline(xintercept = 1, lty = 3) + geom_vline(xintercept = 18, lty = 3) + geom_vline(xintercept = 40, lty = 3) + 
    xlab('Age at Diagnosis (Years)') + ylab('B-ALL Multipotency Score') +
    ggpubr::theme_pubr(legend = 'top') + theme(legend.title = element_blank())
        
p
#ggsave('BALL_MultipotencyScore_Figures/Age_vs_MultipotencyScore_2019patients.pdf', height = 4.2, width = 5.5)
```


## Evaluate on MRD 

```{r}
# Formatted and pivoted
bulk2046_MRD <- bulk2046 %>% 
  select(Patient, MRD_D29_2cat, MRD_D29_3cat, MRD_D29_5cat,  MRD_D46_2cat, 
         c('HSC_MPP', 'Early_Lymphoid', 'Myeloid_Prog', 'Pre_pDC', 'Pro_B', 'Pre_B', 'Mature_B', 'T_NK', 'Monocyte', 'Erythroid',
           'PC1', 'Multipotency_Score')) %>% 
  mutate(MRD_D29_2cat = factor(MRD_D29_2cat, levels = c('Negative\n< 0.01%', 'Positive\n> 0.01%')), 
         MRD_D29_3cat = factor(MRD_D29_3cat, levels = c('< 0.01%', '0.01 - 1%',  '> 1%')), 
         MRD_D29_5cat = factor(MRD_D29_5cat, levels = c('< 0.01%', '0.01 - 0.1%', '0.1 - 1%', '1 - 10%', '> 10%'))) %>% 
  pivot_longer(-c(Patient, MRD_D29_2cat, MRD_D29_3cat, MRD_D29_5cat,  MRD_D46_2cat), names_to = 'Lineage', values_to = 'Score') %>% 
  select(Patient, Lineage, Score, everything()) %>% 
  mutate(Lineage = Lineage %>% #str_replace('NMF.*_', '') %>% 
                factor(levels = c('Multipotency_Score', 'PC1', 'HSC_MPP', 'Early_Lymphoid', 'Myeloid_Prog', 'Pre_pDC', 'Pro_B', 'Pre_B', 
                                  'Mature_B', 'T_NK', 'Monocyte', 'Erythroid'))) %>% 
  arrange(Lineage)

bulk2046_MRD
```

```{r}
p <- bulk2046_MRD %>% 
    filter(MRD_D29_5cat != 'NA') %>% #mutate(Score = ifelse(Lineage %in% c('PC1_a', 'PC1_b', 'PC1_c'), -Score, Score)) %>% 
    filter(Lineage %in% c('Multipotency_Score')) %>% 
    mutate(Lineage = Lineage %>% str_replace('_', ' ')) %>% 
    ggplot(aes(x = MRD_D29_5cat, y = Score, fill = MRD_D29_5cat)) + 
    geom_hline(yintercept = 0, alpha = 0.7, lty = 2) + 
    geom_boxplot(outlier.size=0) + ggbeeswarm::geom_quasirandom(aes(size = MRD_D29_5cat), width=0.3) + 
    facet_wrap(.~Lineage, scales='free', ncol=5) + 
    theme_pubr(legend = 'none') + 
    scale_fill_manual(values = c('#7CA987', '#D9D17D', '#DAA15E', '#FF7F50', '#D43F00')) + 
    theme(strip.text.x = element_text(size = 14), axis.text.x = element_text(size = 11), axis.text.y = element_text(size = 12), axis.title = element_text(size = 12.5)) + 
    scale_size_manual(values = c(0.15, 0.4, 0.4, 0.5, 0.5)) + xlab('\nResidual Disease - Day 29 Induction') + ylab('B-ALL Multipotency Score') + 
    stat_compare_means(comparisons = list(c('< 0.01%', '0.01 - 0.1%'), c('< 0.01%', '0.1 - 1%'), c('< 0.01%', '1 - 10%'), c('< 0.01%', '> 10%')))

p
ggsave('BALL_MultipotencyScore_Figures/MRD_levels_794patients_Multipotency_Score.pdf', device = 'pdf', height = 5.5, width = 4.8)
```


```{r}
p <- bulk2046_MRD %>% 
    filter(MRD_D29_2cat != 'NA') %>% 
    filter(Lineage %in% c('Multipotency_Score')) %>% 
    mutate(Lineage = Lineage %>% str_replace('_', ' ')) %>% 
    ggplot(aes(x = MRD_D29_2cat, y = Score, fill = MRD_D29_2cat)) + 
    geom_hline(yintercept = 0, alpha = 0.7, lty = 2) + 
    geom_boxplot(outlier.size=0) + ggbeeswarm::geom_quasirandom(size = 0.25, width=0.3) + 
    facet_wrap(.~Lineage, scales='free', ncol=5) + 
    theme_pubr(legend = 'none') + 
    scale_fill_manual(values = c('#7CA987', '#D97A6D')) + 
    theme(strip.text.x = element_text(size = 13.5), axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12), axis.title = element_text(size = 12.5)) + 
    xlab('\nResidual Disease - Day 29 Induction') + ylab('B-ALL Multipotency Score') + 
    stat_compare_means(comparisons = list(c('Negative\n< 0.01%', 'Positive\n> 0.01%')))

p
ggsave('BALL_MultipotencyScore_Figures/MRD_Status_1197patients_Multipotency_Score.pdf', device = 'pdf', height = 5, width = 4)
```


## Evaluate on Survival Outcomes 

### Pediatric Survival 

First in Pediatric B-ALL 
Survival outcomes are available for 1,039 pediatric B-ALL patients within the St Jude and COG cohorts.
  Note that the COG patient samples subject for RNA-seq were preselected to be high risk and consequentially these COG outcomes are worse than the "real world"

```{r}
# Keep pediatric cohorts (COG and St Jude)
bulk2046_pediatric <- bulk2046 %>% filter(Institute %in% c('St Jude', 'COG')) %>%
    filter(!is.na(oscensor) & !is.na(efscensor)) %>% 
    mutate(Risk_Group = factor(Risk_Group, levels = c('Childhood SR', 'Childhood HR', 'AYA')),  # clinical risk group
           GenomicRisk_pediatric = factor(GenomicRisk_pediatric, levels = c('Unclassified', 'Favorable', 'Intermediate', 'Unfavorable')))     # genomic risk group

bulk2046_pediatric
```

Loop through developmental states and perform univariable + multivariable cox regression

```{r}
# dataframe to store results
lineage_survival_ped <- data.frame()
devstates <- c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B', 'Mature_B', 'T_NK', 'Monocyte', 'Erythroid')

for(lineage in devstates){

    # Univariable model 
    mod_os <- coxph(Surv(ostime, oscensor) ~ get(lineage), bulk2046_pediatric)
    mod_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage), bulk2046_pediatric)

    lineage_survival_ped <- lineage_survival_ped %>% bind_rows(
        summary(mod_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    ) %>% bind_rows(
        summary(mod_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_efs$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    )
    
    # Multivariable model 
    mod_multi_os <- coxph(Surv(ostime, oscensor) ~ get(lineage) + Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC, bulk2046_pediatric)
    mod_multi_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage) + Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC, bulk2046_pediatric)
    
    # Create baseline models without dev state abundance to compare against by nested LRT
    mod_multi_os_0 <- coxph(Surv(ostime, oscensor) ~ Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC, bulk2046_pediatric)
    mod_multi_efs_0 <- coxph(Surv(efstime, efscensor) ~ Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC , bulk2046_pediatric)

    lineage_survival_ped <- lineage_survival_ped %>% bind_rows(
        summary(mod_multi_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_os, mod_multi_os_0)[2,'P(>|Chi|)'])
        
    ) %>% bind_rows(
      
        summary(mod_multi_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_efs$n) %>%
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_efs, mod_multi_efs_0)[2,'P(>|Chi|)'])
        
    )
}

lineage_survival_ped 
```


```{r, fig.height = 5, fig.width = 6}
surv_ped_uni <- lineage_survival_ped %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == FALSE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% filter(Lineage != 'NA') %>%
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', 'darkgreen', '#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0.3,1.8)) + theme(axis.title.x= element_blank()) + 
    ylab('Univariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Univariable Survival (n = 1039)')
    
surv_ped_multi <- lineage_survival_ped %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue_nestedLRT < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue_nestedLRT < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == TRUE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score','HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% filter(Lineage != 'NA') %>%
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', 'darkgreen', '#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0.3,1.8)) + theme(axis.title.x= element_blank()) + 
    ylab('Multivariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Multivariable Survival (n = 1010)')
    
ggsave(plot = surv_ped_uni, filename = 'BALL_survival_figures_final/Survival_pediatric_Univariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)
ggsave(plot = surv_ped_multi, filename = 'BALL_survival_figures_final/Survival_pediatric_Multivariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)

surv_ped_uni | surv_ped_multi
```

```{r}
bulk2046_pediatric$MRD_D29_2cat %>% table()
```


### Pediatric Survival; MRD negative only
Repeat Pediatric Analysis within MRD negative patients 

```{r}
bulk2046_pediatric_MRDneg <- bulk2046_pediatric %>% filter(MRD_D29_2cat == 'Negative\n< 0.01%')

# dataframe to store results
lineage_survival_ped_MRDneg <- data.frame()
devstates <- c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B', 'Mature_B', 'T_NK', 'Monocyte', 'Erythroid')

for(lineage in devstates){

    # Multivariable model 
    mod_multi_os <- coxph(Surv(ostime, oscensor) ~ get(lineage) + Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC, bulk2046_pediatric_MRDneg)
    mod_multi_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage) + Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC , bulk2046_pediatric_MRDneg)
    
    # Create baseline models without dev state abundance to compare against by nested LRT
    mod_multi_os_0 <- coxph(Surv(ostime, oscensor) ~ Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC, bulk2046_pediatric_MRDneg)
    mod_multi_efs_0 <- coxph(Surv(efstime, efscensor) ~ Risk_Group + GenomicRisk_pediatric + Sex + Age + WBC , bulk2046_pediatric_MRDneg)

    lineage_survival_ped_MRDneg <- lineage_survival_ped_MRDneg %>% bind_rows(
        summary(mod_multi_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_os, mod_multi_os_0)[2,'P(>|Chi|)'])
        
    ) %>% bind_rows(
      
        summary(mod_multi_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_efs$n) %>%
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_efs, mod_multi_efs_0)[2,'P(>|Chi|)'])
        
    )
}

lineage_survival_ped_MRDneg 
```


```{r, fig.height = 5, fig.width = 3}
surv_ped_multi_MRDneg <- lineage_survival_ped_MRDneg %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue_nestedLRT < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue_nestedLRT < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == TRUE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score','HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% filter(Lineage != 'NA') %>%
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', 'darkgreen', '#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0.2,2.1)) + theme(axis.title.x= element_blank()) + 
    ylab('Multivariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Multivariable Survival\n(MRD negative patients; n = 649)')
    
#ggsave(plot = surv_ped_uni, filename = 'BALL_survival_figures_final/Survival_pediatric_Univariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)
#ggsave(plot = surv_ped_multi_MRDneg, filename = 'BALL_survival_figures_final/Survival_pediatric_Multivariable_BDevOnly_HazardRatios_MRDnegative.pdf', device = 'pdf', height = 7.5, width = 4.5)

surv_ped_multi_MRDneg
```

### Adult Survival 

324 adult B-ALL patients with survival data 

```{r}
# Use adult cohort ECOG
bulk2046_adult <- bulk2046 %>% 
    filter(!Institute %in% c('COG', 'St Jude')) %>% 
    filter(!is.na(oscensor) | !is.na(efscensor)) %>% 
    mutate(Risk_Group = factor(Risk_Group, levels = c('AYA', 'Adult')),  # clinical risk group
           GenomicRisk_adult = factor(GenomicRisk_adult, levels = c('Unclassified', 'Favorable', 'Intermediate', 'Unfavorable')))     # genomic risk group

bulk2046_adult
```

Loop through developmental states and perform univariable + multivariable cox regression

```{r}
# dataframe to store results
lineage_survival_adult <- data.frame()
devstates <- c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B', 'Mature_B', 'T_NK', 'Monocyte', 'Erythroid')

for(lineage in devstates){

    # Univariable model stratifiying on institute and primary subtype
    mod_os <- coxph(Surv(ostime, oscensor) ~ get(lineage), bulk2046_adult)
    mod_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage), bulk2046_adult)

    lineage_survival_adult <- lineage_survival_adult %>% bind_rows(
        summary(mod_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    ) %>% bind_rows(
        summary(mod_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_efs$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    )
    
    # Multivariable model stratifiying on institute and primary subtype
    mod_multi_os <- coxph(Surv(ostime, oscensor) ~ get(lineage) + Risk_Group + GenomicRisk_adult + Sex + Age + WBC, bulk2046_adult)
    mod_multi_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage) + Risk_Group + GenomicRisk_adult + Sex + Age + WBC, bulk2046_adult)
    
    # Create baseline models without dev state abundance to compare against by nested LRT
    mod_multi_os_0 <- coxph(Surv(ostime, oscensor) ~ Risk_Group + GenomicRisk_adult + Sex + Age + WBC, bulk2046_adult)
    mod_multi_efs_0 <- coxph(Surv(efstime, efscensor) ~ Risk_Group + GenomicRisk_adult + Sex + Age + WBC, bulk2046_adult)

    lineage_survival_adult <- lineage_survival_adult %>% bind_rows(
        summary(mod_multi_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_os, mod_multi_os_0)[2,'P(>|Chi|)'])
        
    ) %>% bind_rows(
      
        summary(mod_multi_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_efs$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_efs, mod_multi_efs_0)[2,'P(>|Chi|)'])
        
    )
}

lineage_survival_adult
```


```{r, fig.height = 5, fig.width = 6}
surv_adult_uni <- lineage_survival_adult %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == FALSE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'PC1', 'PC1-new', 'HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% filter(Lineage != 'NA') %>%
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', 'darkgreen', '#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0.5,1.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Univariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Univariable - Adult B-ALL (n = 324)')
    
surv_adult_multi <- lineage_survival_adult %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue_nestedLRT < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue_nestedLRT < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == TRUE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'PC1', 'PC1-new', 'HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% filter(Lineage != 'NA') %>%
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0.5,1.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Multivariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Multivariable - Adult B-ALL (n = 312)')
    
#ggsave(plot = surv_adult_uni, filename = 'BALL_survival_figures_final/Survival_adult_Univariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)
#ggsave(plot = surv_adult_multi, filename = 'BALL_survival_figures_final/Survival_adult_Multivariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)

surv_adult_uni | surv_adult_multi
```




### Re-run within BCR::ABL1 patients

```{r}
# Keep BCR::ABL1 patients
bulk2046_ph <- bulk2046 %>% filter(new_Subtype == 'BCR::ABL1')  

bulk2046_ph
bulk2046_ph %>% filter(oscensor %in% c('0','1')) %>% pull(Institute) %>% table()
```


Loop through developmental states and perform univariable + multivariable cox regression

```{r}
# dataframe to store results
lineage_survival_ph <- data.frame()
devstates <- c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B')

for(lineage in devstates){

    # Univariable model stratifiying on institute and primary subtype
    mod_os <- coxph(Surv(ostime, oscensor) ~ get(lineage), bulk2046_ph)
    mod_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage), bulk2046_ph)

    lineage_survival_ph <- lineage_survival_ph %>% bind_rows(
        summary(mod_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    ) %>% bind_rows(
        summary(mod_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_efs$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue)
    )
    
    # Multivariable model stratifiying on institute and primary subtype
    mod_multi_os <- coxph(Surv(ostime, oscensor) ~ get(lineage) + Risk_Group + Sex + Age + WBC, bulk2046_ph)
    mod_multi_efs <- coxph(Surv(efstime, efscensor) ~ get(lineage) + Risk_Group + Sex + Age + WBC, bulk2046_ph)
    
    # Create baseline models without dev state abundance to compare against by nested LRT
    mod_multi_os_0 <- coxph(Surv(ostime, oscensor) ~ Risk_Group + Sex + Age + WBC, bulk2046_ph)
    mod_multi_efs_0 <- coxph(Surv(efstime, efscensor) ~ Risk_Group + Sex + Age + WBC, bulk2046_ph)

    lineage_survival_ph <- lineage_survival_ph %>% bind_rows(
        summary(mod_multi_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_os$n) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_os, mod_multi_os_0)[2,'P(>|Chi|)'])
        
    ) %>% bind_rows(
      
        summary(mod_multi_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'EFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse, 
                   n_patients = mod_multi_efs$n) %>%
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, n_patients, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_efs, mod_multi_efs_0)[2,'P(>|Chi|)'])
        
    )
}

lineage_survival_ph
```


Make forest plots

```{r, fig.height = 5, fig.width = 6}
surv_ph_uni <- lineage_survival_ph %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == FALSE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'HSC/MPP', 'Myeloid Prog', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>%  
                               #'Mature B', 'T/NK', 'Monocyte', 'Erythroid', 'Multipotency Score'))) %>% 
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', '#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0,2.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Univariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Univariable Survival (n = 79)')
    
surv_ph_multi <- lineage_survival_ph %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(Association = ifelse(HR_lwr > 1 & pvalue_nestedLRT < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue_nestedLRT < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == TRUE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'EFS', 'Event-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Event-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'HSC/MPP', 'Myeloid Prog', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>%  
                               #'Mature B', 'T/NK', 'Monocyte', 'Erythroid', 'Multipotency Score'))) %>% 
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('#666666')) +   
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0,2.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Multivariable Hazard Ratio  (per 1SD increase in abundance)') + 
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('Multivariable Survival (n = 76)')

ggsave(plot = surv_ph_uni, filename = 'BALL_survival_figures_final/Survival_Ph_BALL2046_Univariable_BDevOnly_HazardRatios.pdf',
     device = 'pdf', height = 7.5, width = 5.2)
ggsave(plot = surv_ph_multi, filename = 'BALL_survival_figures_final/Survival_Ph_BALL2046_Multivariable_BDevOnly_HazardRatios.pdf',
     device = 'pdf', height = 7.5, width = 5.2)

surv_ph_uni | surv_ph_multi
```



# Kim et al BCR::ABL1 

```{r}
kim_phALL_vst <- read_csv('../subtype_subcluster/Kim2023_Ph_BALL/Kim2023_Ph_BALL_RNAseq_vst.csv')
# calculate NMF scores from vst-normalized data
kim_phALL_vst_DevStatescores <- calculate_DevState_scores(kim_phALL_vst %>% column_to_rownames('Gene') %>% data.matrix() , modelweights_withMultipotency, scale = T, sampleID = 'Sample')
kim_phALL_vst_DevStatescores[1:20, ]
```

```{r}
ph_BALL_combined <- read_csv('../subtype_subcluster/Kim2023_Ph_BALL/Kim2023_Ph_BALL_anno_cleaned.csv') %>% 
  mutate(Sample = ifelse(Manuscript_name %>% str_detect('-R'), paste0(JAMLR, '_nn_M'), paste0(JAMLR, '_nn_P'))) %>% 
  inner_join(kim_phALL_vst_DevStatescores) 

ph_BALL_combined %>% write_csv('Kim2023_Ph_BALL_DevState_Scored_May2024.csv')
ph_BALL_combined
```

Format for survival 

```{r}
ph_BALL_input <- ph_BALL_combined %>% 
  # exclude a diagnosis - relapse pair
  filter(Survival_analysis == 'yes') %>% filter(Age_at_dx > 19) 

ph_BALL_input
```


```{r}
lineage_survival_Ph_Kim <- data.frame()
devstates <- c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B')

for(lineage in devstates){

    # Univariable model stratifiying on institute and primary subtype
    mod_os <- coxph(Surv(OS, alive) ~ get(lineage), ph_BALL_input)
    mod_rfs <- coxph(Surv(RFS, relapse_or_death) ~ get(lineage), ph_BALL_input)

    lineage_survival_Ph_Kim <- lineage_survival_Ph_Kim %>% bind_rows(
        summary(mod_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'OS') %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, statistic, pvalue)
    ) %>% bind_rows(
        summary(mod_rfs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = FALSE, survival = 'RFS') %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, statistic, pvalue)
    )
        
    # Multivariable model stratifiying on institute and primary subtype
    mod_multi_os <- coxph(Surv(OS, alive) ~ get(lineage) + sex + Age_at_dx + WBC, ph_BALL_input)
    mod_multi_efs <- coxph(Surv(RFS, relapse_or_death) ~ get(lineage) + sex + Age_at_dx + WBC, ph_BALL_input)
    
    # Create baseline models without dev state abundance to compare against by nested LRT
    mod_multi_os_0 <- coxph(Surv(OS, alive) ~ sex + Age_at_dx + WBC, ph_BALL_input)
    mod_multi_efs_0 <- coxph(Surv(RFS, relapse_or_death) ~ sex + Age_at_dx + WBC, ph_BALL_input)

    lineage_survival_Ph_Kim <- lineage_survival_Ph_Kim %>% bind_rows(
        summary(mod_multi_os)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'OS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse) %>% 
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_os, mod_multi_os_0)[2,'P(>|Chi|)'])
        
    ) %>% bind_rows(
      
        summary(mod_multi_efs)$coefficients %>% data.frame() %>% dplyr::rename(HR = 'exp.coef.', HRse = 'se.coef.', statistic = 'z', pvalue = 'Pr...z..') %>% 
            rownames_to_column('variable') %>% mutate(Lineage = lineage, multivariable = TRUE, survival = 'RFS') %>% 
            mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse) %>%
            filter(variable %>% str_detect('lineage')) %>% select(Lineage, survival, multivariable, HR, HRse, HR_lwr, HR_upr, statistic, pvalue) %>% 
            # nested LRT: how much prognostic info does dev state add beyond baseline model?
            mutate(pvalue_nestedLRT = anova(mod_multi_efs, mod_multi_efs_0)[2,'P(>|Chi|)'])
        
    )
}

lineage_survival_Ph_Kim
```


```{r, fig.height = 5, fig.width = 3}
lineage_survival_Ph_Kim %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse) %>%
    mutate(Association = ifelse(HR_lwr > 1 & pvalue < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == FALSE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'RFS', 'Relapse-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Relapse-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('PC1_','') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% 
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', 'darkgreen', '#666666')) +   #'#744F80', '#303030'
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0,2.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Univariable Hazard Ratio  (per 1SD increase in abundance)') + #ylim(c(0, 2.5)) +
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('BCR::ABL1 Adult B-ALL (n = 41)')
    
ggsave(filename = 'BALL_survival_figures_final/Survival_Ph_Kim2023_Univariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)
```


```{r, fig.height = 5, fig.width = 3}
lineage_survival_Ph_Kim %>% mutate(logpval = ifelse(HR > 1, -log10(pvalue), log10(pvalue))) %>% 
    mutate(HR_upr = HR + 1.96*HRse, HR_lwr = HR - 1.96*HRse) %>%
    mutate(Association = ifelse(HR_lwr > 1 & pvalue_nestedLRT < 0.05, 'Adverse', ifelse(HR_upr < 1 & pvalue_nestedLRT < 0.05, 'Favorable', 'N.S.'))) %>%
    filter(multivariable == TRUE) %>% 
    mutate(survname = ifelse(survival == 'OS', 'Overall Survival', ifelse(survival == 'RFS', 'Relapse-Free Survival', 'NA')) %>% 
           factor(levels = c('Overall Survival', 'Relapse-Free Survival'))) %>%
    mutate(Lineage = ifelse(Lineage %in% c('Myeloid_Prog', 'Early_Lymphoid', 'Mature_B', 'Multipotency_Score'), Lineage %>% str_replace('Prog', 'Progenitor') %>% str_replace('PC1_','') %>% str_replace('_', ' '), 
                            Lineage %>% str_replace('_MPP', '/MPP') %>% str_replace('_NK', '/NK') %>% str_replace('_', '-')) %>% 
             factor(levels = c('Multipotency Score', 'HSC/MPP', 'Myeloid Progenitor', 'Pre-pDC', 'Early Lymphoid', 'Pro-B', 'Pre-B'))) %>% 
    ggplot(aes(x = Lineage, y = HR, ymax = HR_upr, ymin = HR_lwr, color = Association)) + 
    geom_hline(yintercept = 1, lty = 2, size = 0.5, alpha = 1, color = 'darkgrey') + 
    geom_pointrange(size=0.8, position=position_dodge(width=c(0))) +
    facet_wrap(.~survname, ncol=1, scales = 'free_x') + 
    scale_color_manual(values=c('indianred4', '#666666')) +   #'#744F80', '#303030'
    theme_pubr(legend = 'right') + scale_y_continuous(breaks = scales::pretty_breaks(n=8), limits = c(0,2.5)) + theme(axis.title.x= element_blank()) + 
    ylab('Multivariable Hazard Ratio  (per 1SD increase in abundance)') + #ylim(c(0, 2.5)) +
    theme(axis.text.x = element_text(size = 10.5, angle = 90, hjust = 1, vjust = 0.5), strip.text.x = element_text(size=11), axis.text.y = element_text(size=10.5),
          title = element_text(size = 10.5)) + 
    ggtitle('BCR::ABL1 Adult B-ALL (n = 41)')
    
ggsave(filename = 'BALL_survival_figures_final/Survival_Ph_Kim2023_Multivariable_BDevOnly_HazardRatios.pdf', device = 'pdf', height = 7.5, width = 4.5)
```


# Compare with MPAL

## Score in B-ALL vs MPAL(B/M)

```{r}
panleucohort <- readRDS("../../../../../../../../Other/PanLeuGeneExpr/panLeuTotal-rlog.rds")
panleucohort_anno <- data.table::fread('../../../../../../../../Other/PanLeuGeneExpr/PanLeuCohort.csv') 
panleucohort_BALL_MPAL <- panleucohort[,colnames(panleucohort) %in% filter(panleucohort_anno, disease %in% c('B-ALL', 'MPAL'))$sample]
panleucohort_BALL_MPAL %>% dim()
```

```{r}
panleucohort_rlog_DevStatescores <- calculate_DevState_scores(panleucohort_BALL_MPAL, modelweights_withMultipotency, scale = T, sampleID = 'sample')
panleucohort_rlog_DevStatescores
```


```{r}
panleu_scored <- panleucohort_rlog_DevStatescores %>% left_join(panleucohort_anno)
panleu_scored %>% write_csv('panleucohort_BALL_MPAL_rlog_DevState_scores_May2024.csv')
panleu_scored
```

## Sanity check between datasets

Check correlation between the two datasets - same samples but presumably different alignments and different normalizations. 

```{r, fig.height = 5, fig.width = 12}
panleu_scored %>% filter(disease == 'B-ALL') %>% 
  select(sample, HSC_MPP, Myeloid_Prog, Pre_pDC, Early_Lymphoid, Pro_B, Pre_B, Multipotency_Score) %>% 
  pivot_longer(-sample, values_to = 'rlog') %>% 
  # standardize within B-ALL
  group_by(name) %>% mutate(rlog = (rlog - mean(rlog)) / sd(rlog)) %>% 
  inner_join(
    bulk2046 %>% select(sample = SampleID_old, HSC_MPP, Myeloid_Prog, Pre_pDC, Early_Lymphoid, Pro_B, Pre_B, Multipotency_Score) %>% 
      pivot_longer(-sample, values_to = 'vst')
  ) %>% 
  mutate(name = factor(name, levels = c('Multipotency_Score', 'HSC_MPP', 'Myeloid_Prog', 'Pre_pDC', 'Early_Lymphoid', 'Pro_B', 'Pre_B'))) %>%
  ggplot(aes(x = rlog, y = vst)) + 
  geom_hline(yintercept=0) + geom_vline(xintercept=0) + 
  geom_point(size = 0.5) + geom_smooth(method = 'lm') + stat_cor() + 
  facet_wrap(.~name, ncol = 4) + xlab('rlog normalization (Montefiori et al)') + ylab('vst normalization (B-ALL 2046 cohort)')
```



## Compare Early subsets vs MPAL 

```{r}
pangeneleu_compare <- panleu_scored %>%
  mutate(case_ID = sample %>% str_replace('_.*','')) %>% 
  left_join(read_csv('../subtype_subcluster/Alexander_MPAL_Fusions.csv') %>%
              mutate(ZNF384r = ifelse((gene_a == 'ZNF384') | (gene_b == 'ZNF384'), 1, 0), 
                     KMT2Ar = ifelse((gene_a == 'KMT2A') | (gene_b == 'KMT2A'), 1, 0)) %>% 
              group_by(case_ID) %>% 
              summarise(ZNF384r = ifelse(sum(ZNF384r) >= 1, 'MPAL(ZNF384r)', 'Other'), 
                        KMT2Ar = ifelse(sum(KMT2Ar) >= 1, 'KMT2Ar', 'Other')) %>% unique() ) %>% 
  mutate(disease_subtype = ifelse(is.na(ZNF384r), subtype, ifelse(ZNF384r == 'MPAL(ZNF384r)', ZNF384r, subtype))) %>% 
  left_join( 
    bind_rows(
      read_csv('../subtype_subcluster/KMT2A_subcluster142.csv') %>% mutate(case_ID = Patient %>% str_replace('_.*','')) %>% select(case_ID, subset = Subgroup),
      read_tsv('../subtype_subcluster/DUX4_bulk.txt', col_names=F) %>% select(case_ID = X1, subset = X2))
    ) %>% 
  left_join(
    read_tsv('../survival_analysis_old/Ph_BALL_CellofOrigin_Subtype.tsv') %>% select(sample = Samples, Cluster)
  ) %>% 
  mutate(subset = ifelse(is.na(subset), Cluster, subset))

pangeneleu_compare %>% write_csv("pangeneleu_compare_BALL_MPAL_DevState_May2024.csv")
pangeneleu_compare$subset %>% table()
```


```{r}
pangeneleu_compare <- read_csv("pangeneleu_compare_BALL_MPAL_DevState_May2024.csv")
pangeneleu_compare %>% select(disease_subtype, subset) %>% table()
```


```{r}
# Subset to include B-ALL and B-lineage MPALs
pangeneleu_compare <- pangeneleu_compare %>% filter(disease2 %in% c('B-ALL', 'MPAL(AUL)', 'MPAL(B/M)', 'MPAL(KMT2Ar)', 'MPAL(Ph)')) 
abundances <- c('Multipotency_Score', 'HSC_MPP', 'Early_Lymphoid', 'Myeloid_Prog', 'Pre_pDC', 'Pro_B', 'Pre_B')


# Get categories
MPAL_BALL_categories <- bind_rows(
  # ZNF384
  pangeneleu_compare %>% filter(disease_subtype %>% str_detect('ZNF384')) %>% 
    mutate(disease_category = paste0('ZNF384 ', disease)) %>% 
    select(disease_category, abundances, disease, disease2, subset), 
  # BCR::ABL1
  pangeneleu_compare %>% filter(disease_subtype %>% str_detect('Ph')) %>% 
    mutate(disease_category = paste0('BCR::ABL1 ', ifelse(disease == 'MPAL', 'MPAL',
                                                   ifelse(subset %>% str_detect('Early'), 'Early-Pro', 
                                                          ifelse(subset %>% str_detect('Inter'), 'Inter-Pro',
                                                                 ifelse(subset %>% str_detect('Late'), 'Late-Pro', 'NA')))))) %>% 
    select(disease_category, abundances, disease, disease2, subset) %>% filter(disease_category != 'BCR::ABL1 NA'), 
  # KMT2A
  pangeneleu_compare %>% filter(disease_subtype %>% str_detect('KMT2A')) %>% 
    mutate(disease_category = paste0('KMT2A ', ifelse(disease == 'MPAL', 'MPAL',
                                                      ifelse(subset %>% str_detect('KMT2A-b'), 'Early', 
                                                             ifelse(subset %>% str_detect('KMT2A-a'), 'Committed', 'NA'))))) %>% 
    select(disease_category, abundances, disease, disease2, subset) %>% filter(disease_category != 'KMT2A NA'), 
  # DUX4
  pangeneleu_compare %>% filter(disease_subtype %>% str_detect('DUX4')) %>% 
    mutate(disease_category = ifelse(subset == 'D1', 'DUX4 Committed', ifelse(subset == 'D2', 'DUX4 Early', 'NA'))) %>% 
    select(disease_category, abundances, disease, disease2) %>% filter(disease_category != 'NA'), 
  # Other 
  pangeneleu_compare %>% filter(!disease_subtype %>% str_detect('ZNF384|Ph|KMT2A|DUX4')) %>%
    mutate(disease_category = paste0('Other ', disease)) %>% 
    select(disease_category, abundances, disease, disease2, subset) %>% filter(disease_category != 'NA'), 
  ) 

MPAL_BALL_categories %>% select(disease_category, disease) %>% table()
```


```{r}
cp_multipotency <- cutpointr(MPAL_BALL_categories, x = Multipotency_Score, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
plot(cp_multipotency)
```

```{r}
cutoff <- cp_multipotency$optimal_cutpoint

p <- MPAL_BALL_categories %>% 
   mutate(disease_category = factor(disease_category, levels = rev(c('ZNF384 MPAL', 'ZNF384 B-ALL', 'KMT2A MPAL', 'KMT2A Early', 'BCR::ABL1 MPAL', 'BCR::ABL1 Early-Pro', 
                                                                'Other MPAL', 'DUX4 Early', 'BCR::ABL1 Inter-Pro', 
                                                                'BCR::ABL1 Late-Pro', 'KMT2A Committed', 'DUX4 Committed', 'Other B-ALL')))) %>%
  ggplot(aes(y = disease_category, x = Multipotency_Score, fill=stat(x))) +
    geom_density_ridges_gradient(
        jittered_points = TRUE, scale = 1.7,
        position = position_points_jitter(width = 0, height = 0), 
        point_shape = '|', point_size = 3, point_alpha = 0.3) +
    scale_fill_gradient2(midpoint=cutoff, high='#71305D', low='#5083A2', name = 'Multipotency\nScore') +
    xlab(paste0('Multipotency Score')) +
    theme_pubr() + xlim(-3, 3.7) +
    geom_vline(xintercept = cutoff, lty = 2, alpha=0.5) + 
    ylab('') + 
    theme(legend.position='right', 
          axis.text.y = element_text(size=12, color = c('dodgerblue4', 'dodgerblue4', 'dodgerblue4', 'dodgerblue4', 'dodgerblue4', 'darkgreen', 'indianred4', 
                                                        'darkgreen', 'indianred4', 'darkgreen', 'indianred4', 'darkgreen', 'indianred4')),
          axis.title.x = element_text(size=13)) 
p
```


```{r}
# need MPAL_BALL_categories, Ph, KMT2A, ZNF384
plot_BALL_MPAL_categories <- function(value = 'Multipotency_Score', value_name = 'B-ALL Multipotency Score', cutpoint = cutpoint, ylimits = c(-3, 4)){

  p0 <- MPAL_BALL_categories %>% 
    filter(disease %in% c('B-ALL', 'MPAL')) %>% filter(disease_category %>% str_detect('Other')) %>% 
    mutate(category = 'Other Subtypes') %>% mutate(disease_cat = ifelse(disease == 'MPAL', 'Other\nMPAL', 'Other\nB-ALL') %>% factor(levels = c('Other\nMPAL', 'Other\nB-ALL'))) %>% 
    select(category, disease_cat, value) %>% pivot_longer(-c(category, disease_cat)) %>% 
    ggplot(aes(x = disease_cat, y = value, fill = disease_cat)) + geom_hline(yintercept = cutpoint, lty = 2, alpha = 0.7) +
    geom_boxplot(outlier.size = 0, alpha = 0.8) + ggbeeswarm::geom_quasirandom(aes(size = disease_cat), width = 0.3) + 
    scale_fill_manual(values = c('indianred4', 'darkgreen')) + 
    scale_size_manual(values = c(0.8, 0.15)) + 
    facet_wrap(.~category) + 
    stat_compare_means(comparisons = list(c('Other\nMPAL', 'Other\nB-ALL'))) + 
    theme_pubr(legend = 'none') + ylab(value_name) + xlab('\nSubgroup') + ylim(ylimits) +
    theme(strip.text.x = element_text(size = 13), axis.title = element_text(size = 13), axis.text.x = element_text(size = 12))
  
  p1 <- MPAL_BALL_categories %>% filter(disease_category %>% str_detect('BCR::ABL1')) %>% 
    mutate(disease3 = ifelse(disease2 == 'B-ALL', paste0('BCR::ABL1\n', subset %>% str_replace('Ph_','')), 'BCR::ABL1\nMPAL') %>%  
             factor(levels = rev(c('BCR::ABL1\nLate-Pro', 'BCR::ABL1\nInter-Pro', 'BCR::ABL1\nEarly-Pro', 'BCR::ABL1\nMPAL')))) %>% 
    select(disease3, value) %>% #mutate(disease3 = paste0('BCR::ABL1\n',disease3)) %>% 
    pivot_longer(-disease3) %>% 
    mutate(category = 'BCR::ABL1') %>%
    mutate(`Disease Subgroup` = ifelse(disease3 %>% str_detect('MPAL'), '\nMPAL (B/M)\n', 
                                  ifelse(disease3 %>% str_detect('Early|ZNF384'), '\nB-ALL\nEarly/Multipotent\n',
                                         ifelse(disease3 %>% str_detect('Inter'), '\nB-ALL\nInterPro\n', '\nB-ALL\nCommitted\n'))) %>% 
             factor(levels = c('\nMPAL (B/M)\n', '\nB-ALL\nEarly/Multipotent\n', '\nB-ALL\nInterPro\n', '\nB-ALL\nCommitted\n'))) %>% 
    ggplot(aes(x = disease3, y = value, fill = `Disease Subgroup`)) + geom_hline(yintercept = cutpoint, lty = 2, alpha = 0.7) +
    geom_boxplot(outlier.size = 0, alpha = 0.8) + ggbeeswarm::geom_quasirandom(aes(size = `Disease Subgroup`), width = 0.3) + 
    scale_fill_manual(values = c('indianred4', '#FF7F00', '#5EA2CF', '#1061D9')) + 
    scale_size_manual(values = c(1, 0.6, 0.7, 0.7)) + 
    facet_wrap(.~category) + 
    stat_compare_means(comparisons = list(c('BCR::ABL1\nMPAL', 'BCR::ABL1\nEarly-Pro'), 
                                          c('BCR::ABL1\nMPAL', 'BCR::ABL1\nInter-Pro'),
                                          c('BCR::ABL1\nMPAL', 'BCR::ABL1\nLate-Pro'))) + 
    theme_pubr(legend = 'none') + ylab(value_name) + xlab('\nSubgroup') + ylim(ylimits) +
    theme(strip.text.x = element_text(size = 13), axis.title = element_text(size = 13), axis.text.x = element_text(size = 12))
  
  p2 <- MPAL_BALL_categories %>% filter(disease_category %>% str_detect('KMT2A')) %>% 
    mutate(disease3 = ifelse(disease == 'B-ALL', subset, ifelse(disease == 'MPAL', 'KMT2A-r\nMPAL', subset))) %>% 
    mutate(disease3 = disease3 %>% str_replace('KMT2A-a', 'KMT2A-r\nCommitted') %>% str_replace('KMT2A-b', 'KMT2A-r\nEarly') %>% 
             factor(levels = c('KMT2A-r\nMPAL', 'KMT2A-r\nEarly', 'KMT2A-r\nCommitted'))) %>% filter(disease3 != 'NA') %>% 
    select(disease3, value) %>% #mutate(disease3 = paste0('BCR::ABL1\n',disease3)) %>% 
    pivot_longer(-disease3) %>% 
    mutate(category = 'KMT2A-r') %>%
    mutate(`Disease Subgroup` = ifelse(disease3 %>% str_detect('MPAL'), '\nMPAL (B/M)\n', 
                                  ifelse(disease3 %>% str_detect('Early|ZNF384'), '\nB-ALL\nEarly/Multipotent\n', '\nB-ALL\nCommitted\n')) %>% 
             factor(levels = c('\nMPAL (B/M)\n', '\nB-ALL\nEarly/Multipotent\n', '\nB-ALL\nCommitted\n'))) %>% 
    ggplot(aes(x = disease3, y = value, fill = `Disease Subgroup`)) + geom_hline(yintercept = cutpoint, lty = 2, alpha = 0.7) +
    geom_boxplot(outlier.size = 0, alpha = 0.8) + ggbeeswarm::geom_quasirandom(aes(size = `Disease Subgroup`), width = 0.3) + 
    scale_fill_manual(values = c('indianred4', '#B22122', '#1D90FF')) + 
    scale_size_manual(values = c(1, 0.6, 0.8)) + 
    facet_wrap(.~category) + 
    stat_compare_means(comparisons = list(c('KMT2A-r\nMPAL', 'KMT2A-r\nEarly'), c('KMT2A-r\nMPAL', 'KMT2A-r\nCommitted'))) +
    theme_pubr(legend = 'none') + ylab(value_name) + xlab('\nSubgroup') + ylim(ylimits) +
    theme(strip.text.x = element_text(size = 13), axis.title = element_text(size = 13), axis.text.x = element_text(size = 12))
  
  
  p3 <- MPAL_BALL_categories %>% filter(disease_category %>% str_detect('ZNF384')) %>% 
    mutate(disease3 = paste0('ZNF384-r\n',disease) %>% factor(levels = c('ZNF384-r\nMPAL', 'ZNF384-r\nB-ALL'))) %>% select(disease3, value) %>% 
    pivot_longer(-disease3) %>% 
    mutate(category = 'ZNF384-r') %>%
    mutate(`Disease Subgroup` = ifelse(disease3 %>% str_detect('MPAL'), '\nMPAL (B/M)\n', 
                                  ifelse(disease3 %>% str_detect('Early|ZNF384'), '\nB-ALL\nEarly/Multipotent\n', '\nB-ALL\nCommitted\n')) %>% 
             factor(levels = c('\nMPAL (B/M)\n', '\nB-ALL\nEarly/Multipotent\n', '\nB-ALL\nCommitted\n'))) %>% 
    ggplot(aes(x = disease3, y = value, fill = `Disease Subgroup`)) + geom_hline(yintercept = cutpoint, lty = 2, alpha = 0.7) +
    geom_boxplot(outlier.size = 0, alpha = 0.8) + ggbeeswarm::geom_quasirandom(aes(size = `Disease Subgroup`), width = 0.3) + 
    scale_fill_manual(values = c('indianred4', 'dodgerblue4')) + 
    scale_size_manual(values = c(1, 0.7)) + 
    facet_wrap(.~category) + 
    stat_compare_means(comparisons = list(c('ZNF384-r\nMPAL', 'ZNF384-r\nB-ALL'))) + 
    theme_pubr(legend = 'none') + ylab(value_name) + xlab('\nSubgroup') + ylim(ylimits) +
    theme(strip.text.x = element_text(size = 13), axis.title = element_text(size = 13), axis.text.x = element_text(size = 12))
  
  return(ggarrange(p0, p1, p2, p3, ncol = 4, widths = c(0.58, 1, 0.8, 0.58)))
}

```


```{r, fig.height=3.2, fig.width=10}
plot_BALL_MPAL_categories(value = 'Multipotency_Score', value_name = 'B-ALL Multipotency Score', cutpoint = cp_multipotency$optimal_cutpoint, ylimits = c(-3, 3.75))
ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_MultipotencyScore.pdf', height = 4.8, width = 15, device = 'pdf')

```


```{r, fig.height=3.2, fig.width=10}
cp <- cutpointr(MPAL_BALL_categories, x = HSC_MPP, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
plot_BALL_MPAL_categories(value = 'HSC_MPP', value_name = 'HSC/MPP Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-2.8, 4))
ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_HSCMPP.pdf', height = 4.8, width = 15, device = 'pdf')

```


```{r, fig.height=3.2, fig.width=10}
cp <- cutpointr(MPAL_BALL_categories, x = Early_Lymphoid, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
plot_BALL_MPAL_categories(value = 'Early_Lymphoid', value_name = 'Early Lymphoid Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-2.8, 4))
ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_EarlyLymphoid.pdf', height = 4.8, width = 15, device = 'pdf')

```


```{r, fig.height=3.2, fig.width=10}
cp <- cutpointr(MPAL_BALL_categories, x = Myeloid_Prog, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
plot_BALL_MPAL_categories(value = 'Myeloid_Prog', value_name = 'Myeloid Progenitor Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-2.8, 5))
ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_MyeloidProg.pdf', height = 4.8, width = 15, device = 'pdf')

```


```{r, fig.height=3.2, fig.width=10}
cp <- cutpointr(MPAL_BALL_categories, x = Pre_pDC, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
plot_BALL_MPAL_categories(value = 'Pre_pDC', value_name = 'Pre-pDC Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-2.8, 4))
ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_PrePDC.pdf', height = 4.8, width = 15, device = 'pdf')

```


```{r, fig.height=3.2, fig.width=10}
cp <- cutpointr(MPAL_BALL_categories, x = Pro_B, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
plot_BALL_MPAL_categories(value = 'Pro_B', value_name = 'Pro-B Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-3.5, 3.3))
ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_ProB.pdf', height = 4.8, width = 15, device = 'pdf')

```


```{r, fig.height=3.2, fig.width=10}
cp <- cutpointr(MPAL_BALL_categories, x = Pre_B, class = disease, na.rm = TRUE, 
                method = maximize_metric, metric = sum_sens_spec)
plot_BALL_MPAL_categories(value = 'Pre_B', value_name = 'Pre-B Abundance', cutpoint = cp$optimal_cutpoint, ylimits = c(-3, 3.8))
ggsave('BALL_MultipotencyScore_Figures/BALL_vs_MPAL_bySubtype_ProB.pdf', height = 4.8, width = 15, device = 'pdf')

```



# Score on Pharmacotype Drug Screening Data  

```{r}
# require gene symbol column to be named "Gene"
rpkm_to_logTPM <- function(dat){
  # convert to TPM
  dat_TPM <- dat %>% 
    gather(-Gene, key = "Sample", value = "RPKM") %>%
    group_by(Sample) %>% 
    mutate(logTPM = log1p(RPKM / sum(RPKM) * 1000000)) %>% 
    select(-RPKM) %>% ungroup() %>% 
    spread(Sample, logTPM)
  
  return(dat_TPM)
}

# load pharmacotype data and convert to logTPM
pharmacotype_fpkm <- data.table::fread('../pharmacotypes/pharmacotyping_ped_rnaseq_fpkm_ALLids_0823.csv') %>% select(-GeneID) %>% dplyr::rename(Gene = GeneName)
pharmacotype_logTPM <- pharmacotype_fpkm %>% rpkm_to_logTPM()
pharmacotype_logTPM <- pharmacotype_logTPM %>% column_to_rownames('Gene') %>% data.matrix()
pharmacotype_logTPM %>% dim()
```


```{r}
pharmacotype_logTPM_scored <- calculate_DevState_scores(pharmacotype_logTPM, modelweights_withMultipotency, scale = T, sampleID = 'Patient ID')
pharmacotype_logTPM_scored %>% write_csv('ALL_pharmacotypes_logTPM_DevState_scores_May2024.csv')
pharmacotype_logTPM_scored
```





## Which version of PC1 best separates subtypes within KMT2A, DUX4, BCR::ABL1?

```{r}
bulk2046_subtype_subcluster <- bulk2046 %>% filter(new_Subtype %in% c('BCR::ABL1', 'KMT2A', 'DUX4')) %>% select(Patient, PatientID = PatientID_old, new_Subtype, 
                                                                                                                Multipotency_Score, HSC_MPP, Early_Lymphoid, Pro_B, Pre_B, 
                                                                                                                Institute, oscensor, ostime, efscensor, efstime) %>% 
  left_join( read_tsv("../subtype_subcluster/Ph_sub_clusters_S2046.txt", col_names = c('PatientID', 'Class')) %>% dplyr::rename(Ph_Class = Class), by = 'PatientID') %>% 
  left_join( read_tsv("../subtype_subcluster/DUX4_bulk.txt", col_names = c('PatientID', 'Class')) %>% dplyr::rename(DUX4_Class = Class), by = 'PatientID') %>%
  left_join( read_csv("../subtype_subcluster/KMT2A_subcluster142.csv") %>% select(Patient, KMT2A_Class = Subgroup), by = 'Patient') %>% 
  arrange(new_Subtype) 

bulk2046_subtype_subcluster$KMT2A_Class %>% table()
```


```{r}
cutpointr(bulk2046_subtype_subcluster$HSC_MPP, bulk2046_subtype_subcluster$KMT2A_Class, na.rm=TRUE)
cutpointr(bulk2046_subtype_subcluster$Early_Lymphoid, bulk2046_subtype_subcluster$KMT2A_Class, na.rm=TRUE)
cutpointr(bulk2046_subtype_subcluster$Multipotency_Score, bulk2046_subtype_subcluster$KMT2A_Class, na.rm=TRUE)
```

```{r}
cutpointr(bulk2046_subtype_subcluster$HSC_MPP, bulk2046_subtype_subcluster$DUX4_Class, na.rm=TRUE)
cutpointr(bulk2046_subtype_subcluster$Early_Lymphoid, bulk2046_subtype_subcluster$DUX4_Class, na.rm=TRUE)
cutpointr(bulk2046_subtype_subcluster$Multipotency_Score, bulk2046_subtype_subcluster$DUX4_Class, na.rm=TRUE)
```

```{r}
temp <- bulk2046_subtype_subcluster %>% filter(Ph_Class %>% str_detect("Early|Late"))

cutpointr(temp$HSC_MPP, temp$Ph_Class, na.rm=TRUE)
cutpointr(temp$Early_Lymphoid, temp$Ph_Class, na.rm=TRUE)
cutpointr(temp$Multipotency_Score, temp$Ph_Class, na.rm=TRUE)
```

```{r}
temp <- bulk2046_subtype_subcluster %>% filter(Ph_Class %>% str_detect("Early|Inter"))

cutpointr(temp$HSC_MPP, temp$Ph_Class, na.rm=TRUE)
cutpointr(temp$Early_Lymphoid, temp$Ph_Class, na.rm=TRUE)
cutpointr(temp$Multipotency_Score, temp$Ph_Class, na.rm=TRUE)
```

```{r}
temp <- bulk2046_subtype_subcluster %>% filter(Ph_Class %>% str_detect("Late|Inter"))

cutpointr(temp$HSC_MPP, temp$Ph_Class, na.rm=TRUE)
cutpointr(temp$Early_Lymphoid, temp$Ph_Class, na.rm=TRUE)
cutpointr(temp$Multipotency_Score, temp$Ph_Class, na.rm=TRUE)
```








